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Dr. SWAPAN TALUKDAR
University of Gour Banga

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0 Remote Sensing Applications
0 Wetlands
0 Machine Learning Applications to Earth Sciences
0 Hydrologic and Water Resource Modeling and Simulation
0 Natural Hazarads

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Original paper
Published: 25 August 2021 in Arabian Journal of Geosciences
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The present study aims to investigate the dynamics of land use and land cover (LULC) and their relationship with ecosystem services value (ESV) from the past to the future (1990–2028) in Abha-Khamis twin city of Saudi Arabia. The support vector machine (SVM) was used to classify LULC maps over the years 1990–2018, and their dynamics were examined using delta change and a Markovian transitional probability matrix (TPM). From 1990 to 2018, the ESV was calculated for each LULC type using a coefficient. An artificial neural network-cellular automata model was used to predict the future LULC map for 2028. Sensitivity analysis has been performed using the probability distribution function, Pearson’s correlation methods, random forest, and classification and regression tree. Future LULC was used to derive future ESV from different ecosystems. The results of LULC maps showed that urban areas rose by 334.4% between 1990 and 2018. Delta change rate showed that urban areas have increased by 16.34% since 1990, while the TPM for the period of 1990–2018 reported that the built-up area was the largest stable LULC with a TPM value of 83.6%, while agricultural land, scrubland, exposed rocks, and water bodies were converted by 17.9%, 21.8%, 12.4%, and 10.5%, respectively, into built-up areas. Due to the accelerated and continuous urbanization process, all-natural resources and ecosystem services have been diminished considerably except for dense vegetation. The future LULC map of 2028 showed that the built-up area would be 343.72 km2, followed by scrubland (342.98 km2). The new urban area in 2028 would be 169 km2. The sensitivity analysis showed that proximity to the urban area, vegetation, and scrubland are highly sensitive to simulating and predicting the LULC maps of 2018 and 2028. The authorities and planners should focus more on the sustainable development of the urban areas; otherwise, it would harm both the natural and urban environments.

ACS Style

Ahmed Ali Bindajam; Javed Mallick; Swapan Talukdar; Abu Reza Md. Towfiqul Islam; Saeed Alqadhi. Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: past to future perspective. Arabian Journal of Geosciences 2021, 14, 1 -23.

AMA Style

Ahmed Ali Bindajam, Javed Mallick, Swapan Talukdar, Abu Reza Md. Towfiqul Islam, Saeed Alqadhi. Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: past to future perspective. Arabian Journal of Geosciences. 2021; 14 (18):1-23.

Chicago/Turabian Style

Ahmed Ali Bindajam; Javed Mallick; Swapan Talukdar; Abu Reza Md. Towfiqul Islam; Saeed Alqadhi. 2021. "Integration of artificial intelligence–based LULC mapping and prediction for estimating ecosystem services for urban sustainability: past to future perspective." Arabian Journal of Geosciences 14, no. 18: 1-23.

Research article
Published: 14 August 2021 in Environmental Science and Pollution Research
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Landslides and other disastrous natural catastrophes jeopardise natural resources, assets, and people’s lives. As a result, future resource management will necessitate landslide susceptibility mapping (LSM) using the best conditioning factors. In Aqabat Al-Sulbat, Asir province, Saudi Arabia, the goal of this study was to find optimal conditioning parameters dependent hybrid LSM. LSM was created using machine learning methods such as random forest (RF), logistic regression (LR), and artificial neural network (ANN). To build ensemble models, the LR was combined with RF and ANN models. The receiver operating characteristic (ROC) curve was used to validate the LSMs and determine which models were the best. Then, utilising random forest (RF), classification and regression tree (CART), and correlation feature selection, sensitivity analysis was carried out. Through sensitivity analysis, the most relevant conditioning factors were determined, and the best model was applied to the important parameters to build a highly robust LSM with fewer variables. The ROC curve was also used to evaluate the final model. The results show that two hybrid models (LR-ANN and LR-RF) were predicted the very high as 29.67–32.73 km2 and high LS regions as 21.84–33.38 km2, with LR predicting 22.34km2 as very high and 45.15km2 as high LS zones. The LR-RF appeared as best model (AUC: 0.941), followed by LR-ANN (AUC: 0.915) and LR (AUC: 0.872). Sensitivity analysis, on the other hand, allows for the exclusion of aspects, hillshade, drainage density, curvature, and TWI from LSM. The LSM was then predicted using the LR-RF model based on the remaining nine conditioning factors. With fewer variables, this model has achieved greater accuracy (AUC: 0.927). This comes very close to being the best hybrid model. As a result, it is strongly advised to choose conditioning parameters with caution, as redundant parameters would result in less resilient LSM. As a consequence, both time and resources would be saved, and precise LSM would indeed be possible.

ACS Style

Saeed Alqadhi; Javed Mallick; Swapan Talukdar; Ahmed Ali Bindajam; Nguyen Van Hong; Tamal Kanti Saha. Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environmental Science and Pollution Research 2021, 1 -20.

AMA Style

Saeed Alqadhi, Javed Mallick, Swapan Talukdar, Ahmed Ali Bindajam, Nguyen Van Hong, Tamal Kanti Saha. Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environmental Science and Pollution Research. 2021; ():1-20.

Chicago/Turabian Style

Saeed Alqadhi; Javed Mallick; Swapan Talukdar; Ahmed Ali Bindajam; Nguyen Van Hong; Tamal Kanti Saha. 2021. "Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia." Environmental Science and Pollution Research , no. : 1-20.

Journal article
Published: 26 July 2021 in Journal of Environmental Management
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Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

ACS Style

Tamal Kanti Saha; Swades Pal; Swapan Talukdar; Sandipta Debanshi; Rumki Khatun; Pankaj Singha; Indrajit Mandal. How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region. Journal of Environmental Management 2021, 297, 113344 .

AMA Style

Tamal Kanti Saha, Swades Pal, Swapan Talukdar, Sandipta Debanshi, Rumki Khatun, Pankaj Singha, Indrajit Mandal. How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region. Journal of Environmental Management. 2021; 297 ():113344.

Chicago/Turabian Style

Tamal Kanti Saha; Swades Pal; Swapan Talukdar; Sandipta Debanshi; Rumki Khatun; Pankaj Singha; Indrajit Mandal. 2021. "How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region." Journal of Environmental Management 297, no. : 113344.

Journal article
Published: 04 July 2021 in Remote Sensing
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The present paper proposes a novel fuzzy-VORS (vigor, organization, resilience, ecosystem services) model by integrating fuzzy logic and a VORS model to predict ecosystem health conditions in Abha city of Saudi Arabia from the past to the future. In this study, a support vector machine (SVM) classifier was utilized to classify the land use land cover (LULC) maps for 1990, 2000, and 2018. The LULCs dynamics in 1990–2000, 2000–2018, and 1990–2018 were computed using delta (Δ) change and Markovian transitional probability matrix. The future LULC map for 2028 was predicted using the artificial neural network-cellular automata model (ANN-CA). The machine learning algorithms, such as random forest (RF), classification and regression tree (CART), and probability distribution function (PDF) were utilized to perform sensitivity analysis. Pearson’s correlation technique was used to explore the correlation between the predicted models and their driving variables. The ecosystem health conditions for 1990–2028 were predicted by integrating the fuzzy inference system with the VORS model. The results of LULC maps showed that urban areas increased by 334.4% between 1990 and 2018. Except for dense vegetation, all the natural resources and generated ecosystem services have been decreased significantly due to the rapid and continuous urbanization process. A future LULC map (2028) showed that the built-up area would be 343.72 km2. The new urban area in 2028 would be 169 km2. All techniques for sensitivity analysis showed that proximity to urban areas, vegetation, and scrubland are highly sensitive to land suitability models to simulate and predict LULC maps of 2018 and 2028. Global sensitivity analysis showed that fragmentation or organization was the most sensitive parameter for ecosystem health conditions.

ACS Style

Javed Mallick; Saeed AlQadhi; Swapan Talukdar; Biswajeet Pradhan; Ahmed Bindajam; Abu Islam; Amal Dajam. A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia. Remote Sensing 2021, 13, 2632 .

AMA Style

Javed Mallick, Saeed AlQadhi, Swapan Talukdar, Biswajeet Pradhan, Ahmed Bindajam, Abu Islam, Amal Dajam. A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia. Remote Sensing. 2021; 13 (13):2632.

Chicago/Turabian Style

Javed Mallick; Saeed AlQadhi; Swapan Talukdar; Biswajeet Pradhan; Ahmed Bindajam; Abu Islam; Amal Dajam. 2021. "A Novel Technique for Modeling Ecosystem Health Condition: A Case Study in Saudi Arabia." Remote Sensing 13, no. 13: 2632.

Article
Published: 21 June 2021 in Environment, Development and Sustainability
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According to the World Urbanization Prospects of United Nations, the global urban population has increased rapidly over past few decades, reaching about 55% in 2018, which is projected to reach 68% by 2050. Due to gradual increase in the urban population and impervious surfaces, the urban heat island (UHI) effect has increased manifold in the cities of developing countries, causing a decline in thermal comfort. Therefore, this study was designed to model the spatio-temporal pattern of UHI and its relationships with the land use indices of Delhi and Mumbai metro cities from 1991 to 2018. Landsat datasets were used to generate the land surface temperature (LST) using mono window algorithm and land use indices, such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBal), normalized difference moisture index (NDMI), and modified normalized difference water index (MNDWI). Additionally, the urban hotspots (UHS) were identified and then the thermal comfort was modelled using the UTFVI. The results showed that maximum (30.25–38.99 °C in Delhi and 42.10–45.75 °C in Mumbai) and minimum (17.70–23.86 °C in Delhi and 19.06–25.05 °C in Mumbai) LST witnessed steady growth in Delhi and Mumbai from 1991 to 2018. The LST gap decreases and the UHI zones are being established in both cities. Furthermore, the UHS and worst-category UTFVI areas increased in both cities. This research can be useful in designing urban green-space planning strategies for mitigating the UHI effects and thermal comfort in cities of developing countries.

ACS Style

Shahfahad; Swapan Talukdar; Mohd. Rihan; Hoang Thi Hang; Sunil Bhaskaran; Atiqur Rahman. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environment, Development and Sustainability 2021, 1 -29.

AMA Style

Shahfahad, Swapan Talukdar, Mohd. Rihan, Hoang Thi Hang, Sunil Bhaskaran, Atiqur Rahman. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environment, Development and Sustainability. 2021; ():1-29.

Chicago/Turabian Style

Shahfahad; Swapan Talukdar; Mohd. Rihan; Hoang Thi Hang; Sunil Bhaskaran; Atiqur Rahman. 2021. "Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities." Environment, Development and Sustainability , no. : 1-29.

Journal article
Published: 12 June 2021 in Ecological Informatics
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The current study aimed to investigate the vulnerability state of wetland habitat as a result of damming. Wetland habitat vulnerability state (WHVS) models for pre and post-dam periods were built to investigate the impact, and the difference was assessed. Sixteen hydrological, land composition and water quality parameters were used for modelling WHVS. Swarm intelligence optimised machine learning algorithms such as SVM (Support Vector Machine), ANN (Artificial Neural Network), bagging, radial basis (RBF) and M5P model tree were developed. The models' efficiency was evaluated using statistical methods such as the Receiver operating characteristics (ROC) curve. According to the machine learning models, 8.13–14.58% of the area in the wetland fringe area, small patches, and edges was under the very high vulnerable wetland habitat status in the pre-dam period. During the post-dam period, the region covered by fringes and small and medium-core wetlands increased to 21.23–50.58%. The PSO-RBF model was found to be the best representative model. This study provides a large database of wetland habitat conditions, which could aid policymakers in developing wetland conservation and restoration plans.

ACS Style

Rumki Khatun; Swapan Talukdar; Swades Pal; Tamal Kanti Saha; Susanta Mahato; Sandipta Debanshi; Indrajit Mandal. Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming. Ecological Informatics 2021, 64, 101349 .

AMA Style

Rumki Khatun, Swapan Talukdar, Swades Pal, Tamal Kanti Saha, Susanta Mahato, Sandipta Debanshi, Indrajit Mandal. Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming. Ecological Informatics. 2021; 64 ():101349.

Chicago/Turabian Style

Rumki Khatun; Swapan Talukdar; Swades Pal; Tamal Kanti Saha; Susanta Mahato; Sandipta Debanshi; Indrajit Mandal. 2021. "Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming." Ecological Informatics 64, no. : 101349.

Original paper
Published: 13 May 2021 in Theoretical and Applied Climatology
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Changes in precipitation as a result of climate change are becoming a widespread issue all around the world. A lack of rainfall causes a meteorological drought. The short-term Standardized Precipitation Index (SPI-6) index was used to estimate meteorological drought conditions in Saudi Arabia's Asir region from 1970 to 2017. Innovative trend analysis (ITA), the Modified Mann–Kendall test (MMK), the Sequential Mann–Kendall test, and Morlet wavelet transformation were used to detect trend and periodicity in meteorological drought conditions in the Asir region. In addition, the meteorological drought conditions were forecasted by integrating Particle Swarm Optimization (PSO) ensemble machine learning algorithm and an artificial neural network (ANN). Droughts of varying severity have become more frequent in Asir, according to the findings. In most stations, ITA and MMK tests have revealed a significant increase in drought. In all stations, the SQMK test revealed a big sudden year-over-year drought trend. With the exception of one station, all stations experienced extreme drought frequency discovered using Morlet Wavelet Transformation over a long period of time (10 years or more) (station 34). The PSO-ANN hybrid learning algorithm predicted SPI-6 values that had a strong correlation with actual SPI-6 values and also had lower error values, indicating that this model performed well. The PSO-ANN model predicts that the Asir region of Saudi Arabia will experience major moderate to extreme drought events in the coming years (2018–2025). The findings of this analysis will assist planners and policymakers in planning for the acquisition of sustainable agriculture in the study area.

ACS Style

Majed Alsubih; Javed Mallick; Swapan Talukdar; Roquia Salam; Saeed AlQadhi; Abdul Fattah; Nguyen Viet Thanh. An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia. Theoretical and Applied Climatology 2021, 145, 597 -617.

AMA Style

Majed Alsubih, Javed Mallick, Swapan Talukdar, Roquia Salam, Saeed AlQadhi, Abdul Fattah, Nguyen Viet Thanh. An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia. Theoretical and Applied Climatology. 2021; 145 (1-2):597-617.

Chicago/Turabian Style

Majed Alsubih; Javed Mallick; Swapan Talukdar; Roquia Salam; Saeed AlQadhi; Abdul Fattah; Nguyen Viet Thanh. 2021. "An investigation of the short-term meteorological drought variability over Asir Region of Saudi Arabia." Theoretical and Applied Climatology 145, no. 1-2: 597-617.

Journal article
Published: 24 April 2021 in Heliyon
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Soil erosion is one of the main threats facing the agriculture and natural resources sector all over the world, and the same is true for Syria. Several empirical and physically based tools have been proposed to assess erosion induced soil losses and runoff driving the processes, from plot to regional spatial scales. The main goal of this research is to evaluate the performance of the Water Erosion Prediction Project (WEPP) model in predicting runoff in comparison with field experiments in the Al-Sabahia region of Western Syria in three ecosystems: agricultural lands (AG), burned forest (BF) and forest (FO). To achieve this, field experimental plots (2∗1.65∗0.5 m) were prepared to obtain runoff observation data between September 2012 and December 2013. In addition, the input data (atmospheric forcing, soil, slope, land management) were prepared to run the WEPP model to estimate the runoff. The results indicate that the average observed runoffs in the AG, BF and FO were 12.54 ± 1.17, 4.81 ± 0.97 and 1.72 ± 0.16 mm/event, respectively, while the simulated runoffs in the AG, BF and FO were 15.15 ± 0.89, 9.23 ± 1.48 and 2.61 ± 0.47mm/event, respectively. The statistical evaluation of the model's performance showed an unsatisfactory performance of the WEPP model for predicting the run-offs in the study area. This may be caused by the structural flaws in the model, and/or the insufficient site-specific input parameters. So, to achieve good performance and reliable results of the WEPP model, more observation data is required from different ecosystems in Syria. These findings can provide guidance to planners and environmental engineers for proposing environmental protection and water resources management plans in the Coastal Region in Syria.

ACS Style

Safwan Mohammed; Mais Hussien; Karam Alsafadi; Ali Mokhtar; Guido Rianna; Issa Kbibo; Mona Barkat; Swapan Talukdar; Szilárd Szabó; Endre Harsanyi. Assessing the WEPP model performance for predicting daily runoff in three terrestrial ecosystems in western Syria. Heliyon 2021, 7, e06764 .

AMA Style

Safwan Mohammed, Mais Hussien, Karam Alsafadi, Ali Mokhtar, Guido Rianna, Issa Kbibo, Mona Barkat, Swapan Talukdar, Szilárd Szabó, Endre Harsanyi. Assessing the WEPP model performance for predicting daily runoff in three terrestrial ecosystems in western Syria. Heliyon. 2021; 7 (4):e06764.

Chicago/Turabian Style

Safwan Mohammed; Mais Hussien; Karam Alsafadi; Ali Mokhtar; Guido Rianna; Issa Kbibo; Mona Barkat; Swapan Talukdar; Szilárd Szabó; Endre Harsanyi. 2021. "Assessing the WEPP model performance for predicting daily runoff in three terrestrial ecosystems in western Syria." Heliyon 7, no. 4: e06764.

Journal article
Published: 23 March 2021 in Environmental Pollution
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Global temperature rises in response to accumulating greenhouse gases is a well-debated issue in the present time. Historical records show that greenhouse gases positively influence temperature. Lockdown incident has brought an opportunity to justify the relation between greenhouse gas centric air pollutants and climatic variables considering a concise period. The present work has intended to explore the trend of air quality parameters, and air quality induced risk state since pre to during the lockdown period in reference to India and justifies the influence of pollutant parameters on climatic variables. Results showed that after implementation of lockdown, about 70% area experienced air quality improvement during the lockdown. The hazardous area was reduced from 7.52% to 5.17%. The spatial association between air quality components and climatic variables were not found very strong in all the cases. Still, statistically, a significant relation was observed in the case of surface pressure and moisture. From this, it can be stated that pollutant components can control the climatic components. This study recommends that pollution source management could be a partially good step for bringing climatic resilience of a region.

ACS Style

Susanta Mahato; Swapan Talukdar; Swades Pal; Sandipta Debanshi. How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India. Environmental Pollution 2021, 280, 116975 .

AMA Style

Susanta Mahato, Swapan Talukdar, Swades Pal, Sandipta Debanshi. How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India. Environmental Pollution. 2021; 280 ():116975.

Chicago/Turabian Style

Susanta Mahato; Swapan Talukdar; Swades Pal; Sandipta Debanshi. 2021. "How far climatic parameters associated with air quality induced risk state (AQiRS) during COVID-19 persuaded lockdown in India." Environmental Pollution 280, no. : 116975.

Journal article
Published: 17 March 2021 in Journal of Cleaner Production
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Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 μg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

ACS Style

Swades Pal; Priyanka Das; Indrajit Mandal; Rajesh Sarda; Susanta Mahato; Kim-Anh Nguyen; Yuei-An Liou; Swapan Talukdar; Sandipta Debanshi; Tamal Kanti Saha. Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India. Journal of Cleaner Production 2021, 297, 126674 .

AMA Style

Swades Pal, Priyanka Das, Indrajit Mandal, Rajesh Sarda, Susanta Mahato, Kim-Anh Nguyen, Yuei-An Liou, Swapan Talukdar, Sandipta Debanshi, Tamal Kanti Saha. Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India. Journal of Cleaner Production. 2021; 297 ():126674.

Chicago/Turabian Style

Swades Pal; Priyanka Das; Indrajit Mandal; Rajesh Sarda; Susanta Mahato; Kim-Anh Nguyen; Yuei-An Liou; Swapan Talukdar; Sandipta Debanshi; Tamal Kanti Saha. 2021. "Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India." Journal of Cleaner Production 297, no. : 126674.

Journal article
Published: 04 March 2021 in Journal of Cleaner Production
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Although the impact of lockdown on Air Quality Index(AQI) was given enough attention but investigation on AQI during partial lockdown, change of the worst AQI hot spot pattern and its consistency, spatio-temporal dynamics of core-periphery divide of pollution over megacities were lacking. The present study explored the above mentioned issues along with monitoring of AQI of India during lockdown and partial lockdown based on the daily data of Central Pollution Control Board(CBCB). Gi-index, instability index, least squares regression and frequency approaches were used for analyzing AQI hot spot, spatial instability of AQI, trend of AQI and consistency of Pollution State Presence Frequency (PSPF). In result, clear improvement of AQI was observed since average AQI reduced from 110 in pre-lockdown to 73 in lockdown I and 93 in partial lockdown. The average AQI of the mega cities was improved up to 55%–75% in lockdown. However during partial lockdown, with restoration of economic activities the air quality was observed to degrade again. AQI hotspot and PSPF were identified high in and around Delhi and industrial hubs. Positive trend of AQI change, instability of AQI were found gradually high in partial lockdown period and these effects was observed greater in urban and industrial belts. Though all these facts signify anthropogenic activities as a major source of air pollution but shutting down economic activities lockdown couldn’t be a permanent solution to combat it. Hence, prioritizing green energy sources, improve technologies, utilize energy sustainably that could reduce the pollution level.

ACS Style

Priyanka Das; Indrajit Mandal; Sandipta Debanshi; Susanta Mahato; Swapan Talukdar; Biplab Giri; Swades Pal. Short term unwinding lockdown effects on air pollution. Journal of Cleaner Production 2021, 296, 126514 .

AMA Style

Priyanka Das, Indrajit Mandal, Sandipta Debanshi, Susanta Mahato, Swapan Talukdar, Biplab Giri, Swades Pal. Short term unwinding lockdown effects on air pollution. Journal of Cleaner Production. 2021; 296 ():126514.

Chicago/Turabian Style

Priyanka Das; Indrajit Mandal; Sandipta Debanshi; Susanta Mahato; Swapan Talukdar; Biplab Giri; Swades Pal. 2021. "Short term unwinding lockdown effects on air pollution." Journal of Cleaner Production 296, no. : 126514.

Journal article
Published: 22 February 2021 in Geoscience Frontiers
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The flood hazard management is one of the major challenges in the floodplain regions worldwide. With the rise in population growth and the spread of infrastructural development, the level of risk has increased over time. Therefore, the prediction of flood susceptible area is a key challenge for the adoption of management plans. Flood susceptibility modeling is technically a common work, but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner. Therefore, the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network (ANN), radial basis function (RBF), random forest (RF) and their ensemble-based flood susceptibility models. The flood susceptible models were constructed based on nine flood conditioning parameters. The flood susceptibility models were validated in a conventional way using the receiver operating curve (ROC). To validate the flood-susceptible models, a two dimensional (2D) hydraulic flood simulation model was developed. Also, the index of flood vulnerability model was developed and applied for validating the flood susceptible models, which was a very unique way to validate the predictive models. Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models. Results showed that 11.95%–12.99% of the entire basin area (10188.4 km2) comes under very high flood-susceptible zones. Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models. The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models. Therefore, the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.

ACS Style

Susanta Mahato; Swades Pal; Swapan Talukdar; Tamal Kanti Saha; Parikshit Mandal. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models. Geoscience Frontiers 2021, 12, 101175 .

AMA Style

Susanta Mahato, Swades Pal, Swapan Talukdar, Tamal Kanti Saha, Parikshit Mandal. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models. Geoscience Frontiers. 2021; 12 (5):101175.

Chicago/Turabian Style

Susanta Mahato; Swades Pal; Swapan Talukdar; Tamal Kanti Saha; Parikshit Mandal. 2021. "Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models." Geoscience Frontiers 12, no. 5: 101175.

Journal article
Published: 20 February 2021 in Journal of Environmental Management
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Along with wetland loss, the damming effect on hydrological modification in wetland is another less debated and challenging topic, which needs to have urgent attention. The present work intended to investigate the damming effect on the water richness and eco-hydrological condition of the floodplain wetland and its consequent ecological responses in Punarbhaba River Basin of India and Bangladesh. Satellite images derived hydro-period, water presence frequency (WPF), and water depth were generated for developing water richness model in pre (up to 1992) and post dam conditions (1993–2019). The range of variability (RVA) was modelled using time series satellite images based water index or normalized difference water index (NDWI). Based on RVA model, the hydrological failure rate was developed. Depth of water was used for preparing the flow duration curve (FDC) to estimate the eco-hydro-deficit and surplus condition in wetland at spatial scale for pre and post-dam periods. Satellite image based trophic state index models for pre and post dam conditions were constructed to investigate the ecological response of dam on floodplain wetlands. Results of water richness model showed that wetlands area under high wetland water richness zone decreased from 71.83% to 7.65% in the post-dam conditions. Results of hydrological failure rate showed that high failure rate captured 45% of total wetland area in the post-dam period. Results of eco-hydro-deficit exhibited that eco-hydro-deficit areas increased from 11.22% to 52.19% and 35.03%–52.67% respectively in post-dam conditions indicating growing ecological stress. The TSI models showed that most parts of the wetlands were converted from oligotrophic to meso-eutrophic state signifying the qualitative degradation of water and potential ecosystem health. The area under high TSI was observed in the wetland area having eco-hydro-deficit and high hydrological failure rate zones. These characteristics of wetland areas were found at the fringe of wetlands and fragmented smaller wetland units. The study concluded that damming over the Punarbhaba River adversely affected the water security of the floodplain wetlands in terms of modifying the hydrological richness, ecological condition of the wetland habitat, and ecological systems. The findings of the present study could provide a comprehensive research on the monitoring of surface water crisis in the wetlands, which will be the basic foundation to formulate water resource management plans for conservation, management and restoration of wetlands.

ACS Style

Rumki Khatun; Swapan Talukdar; Swades Pal; Sonali Kundu. Measuring dam induced alteration in water richness and eco-hydrological deficit in flood plain wetland. Journal of Environmental Management 2021, 285, 112157 .

AMA Style

Rumki Khatun, Swapan Talukdar, Swades Pal, Sonali Kundu. Measuring dam induced alteration in water richness and eco-hydrological deficit in flood plain wetland. Journal of Environmental Management. 2021; 285 ():112157.

Chicago/Turabian Style

Rumki Khatun; Swapan Talukdar; Swades Pal; Sonali Kundu. 2021. "Measuring dam induced alteration in water richness and eco-hydrological deficit in flood plain wetland." Journal of Environmental Management 285, no. : 112157.

Research article
Published: 13 January 2021 in Geocarto International
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The present work intended to model the vegetation health index (VHI) of rabi crop and explored its time series change (1990–2019) at pixel scale using Landsat satellite imageries. Identification of consistency level of rabi crop was another objective. VHI was estimated by integrating crop condition index (CCI) and temperature condition index (TCI). The CCI and TCI were derived from normalized difference vegetation index (NDVI) and land surface temperature (LST). 80% of the study area covered by rabi crop with 8.73% of variability. Remarkable change in crop health (VHI) was detected in the last 30 years. 5% of rabi cropped area was under good health category in the present decade (2010–2019), while it was around 10% during 1990–2009. During last decade, the poor health category of rabi crop was only 7.40% area, while it reached to 66.74% in the present decade. Good VHI area was getting fragmented and vice versa.

ACS Style

Swades Pal; Pallabi Chowdhury; Swapan Talukdar; Rajesh Sarda. Modelling rabi crop health in flood plain region of India using time-series Landsat data. Geocarto International 2021, 1 -28.

AMA Style

Swades Pal, Pallabi Chowdhury, Swapan Talukdar, Rajesh Sarda. Modelling rabi crop health in flood plain region of India using time-series Landsat data. Geocarto International. 2021; ():1-28.

Chicago/Turabian Style

Swades Pal; Pallabi Chowdhury; Swapan Talukdar; Rajesh Sarda. 2021. "Modelling rabi crop health in flood plain region of India using time-series Landsat data." Geocarto International , no. : 1-28.

Journal article
Published: 06 January 2021 in Sustainability
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Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km2 were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km2 at the 2 year return period and 40 km2 at the 100 year return period for the high landslide hazard zone, and 6 km2 at the 2 year return period and 20 km2 at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km2 to 62 km2). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.

ACS Style

Javed Mallick; Saeed Alqadhi; Swapan Talukdar; Majed AlSubih; Mohd. Ahmed; Roohul Khan; Nabil Kahla; Saud Abutayeh. Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms. Sustainability 2021, 13, 457 .

AMA Style

Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Majed AlSubih, Mohd. Ahmed, Roohul Khan, Nabil Kahla, Saud Abutayeh. Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms. Sustainability. 2021; 13 (2):457.

Chicago/Turabian Style

Javed Mallick; Saeed Alqadhi; Swapan Talukdar; Majed AlSubih; Mohd. Ahmed; Roohul Khan; Nabil Kahla; Saud Abutayeh. 2021. "Risk Assessment of Resources Exposed to Rainfall Induced Landslide with the Development of GIS and RS Based Ensemble Metaheuristic Machine Learning Algorithms." Sustainability 13, no. 2: 457.

Journal article
Published: 09 December 2020 in Remote Sensing Applications: Society and Environment
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Landscape structure or fragmentations have important effects on ecosystem services, with a common assumption being that fragmentation effects can reduce ecosystem services provision. Impact of Land use land cover (LULC) change on ecosystem service value is well explored techniques in recent times, but landscape fragmentation effect on ecosystem services value (ESV) is yet not quantitatively explored. The present work has intended to focus on the fragmentation effect on ESV introducing a new approach along with the effect of LULC change on ESV. All the analysis is done considering four times (1991, 2001, 2011, 2019) from Landsat images. Fragmentation analysis in ArcGis software has generated six hierarchic landscape units like patch, edge, perforated, small core, medium core and large core. For showing fragmentation effect ESV is computed for a typical LULC as a whole using Coefficient value (CV) of Costanza, 1997 and 2014 and ESV of the fragmented landscapes using weighted CV based on Analytic Hierarchy Process (AHP). Result has clearly demonstrated that due to fragmentation of forest and water body, a major means of qualitative degradation of eco-region, ESV of the respective lands have reduced from 24.43 to 20.57 million USD/year from 1991 to 2019. Total ESV of agriculture and built up land were respectively 43.62 and 4.15 million USD in 1991 and it was changed to 37.92 and 5.85 million USD in 2019. Computed ESV of forest without considering fragmentation effect is 14.68 million USD/year but it is diminuend only 5.71 million USD/year if fragmentation effect is considered in 2019. The ESV of the water body is six times lower in the fragmented landscape as per 2019. Anthropogenic effects have a paramount role for growing land use change, fragmentation and change of ESV in a natural landscape.

ACS Style

Swades Pal; Pankaj Singha; Kabita Lepcha; Sandipta Debanshi; Swapan Talukdar; Tamal Kanti Saha. Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment 2020, 21, 100454 .

AMA Style

Swades Pal, Pankaj Singha, Kabita Lepcha, Sandipta Debanshi, Swapan Talukdar, Tamal Kanti Saha. Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment. Remote Sensing Applications: Society and Environment. 2020; 21 ():100454.

Chicago/Turabian Style

Swades Pal; Pankaj Singha; Kabita Lepcha; Sandipta Debanshi; Swapan Talukdar; Tamal Kanti Saha. 2020. "Proposing multicriteria decision based valuation of ecosystem services for fragmented landscape in mountainous environment." Remote Sensing Applications: Society and Environment 21, no. : 100454.

Original paper
Published: 13 November 2020 in Theoretical and Applied Climatology
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The present study is designed to analyse the annual rainfall variability and trend in 30 meteorological stations of the Asir region for the period of 1970–2017 using the Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) test, trend free pre-whitening Mann-Kendall (TFPW MK) test, and the innovative trend analysis (ITA). A comparative study among the trend detection techniques was performed using the correlation coefficient. The future rainfall trend based on the historical rainfall pattern was investigated by using detrended fluctuation analysis (DFA). Results of the MK test showed that 20 stations in the study area observed a negative trend, and out of these, nine stations had significant negative trends at the significance level of 0.01. The findings of the MMK test showed that 23 stations recorded negative trends, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the TFPW-MK test, 21 stations observed a negative trend, and among these, 10 stations had significant negative trends at the significance of 0.01. ITA detected 25 stations observing a negative trend, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the tests and their performance, the MMK test appeared as the best performing technique among the MK test family, while ITA appeared as the best trend detection technique among the four techniques because it outperformed (p < 0.01) the others. Results of DFA showed that 23 stations (10 were significant) had recorded declining future rainfall trends based on past trends. The results of the present study would help the planners and policy makers to make accurate and easy decisions on irrigation, climatic, and water resource management in the Asir region of Saudi Arabia.

ACS Style

Javed Mallick; Swapan Talukdar; Majed Alsubih; Roquia Salam; Mohd Ahmed; Nabil Ben Kahla; Shamimuzzaman. Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theoretical and Applied Climatology 2020, 143, 823 -841.

AMA Style

Javed Mallick, Swapan Talukdar, Majed Alsubih, Roquia Salam, Mohd Ahmed, Nabil Ben Kahla, Shamimuzzaman. Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theoretical and Applied Climatology. 2020; 143 (1-2):823-841.

Chicago/Turabian Style

Javed Mallick; Swapan Talukdar; Majed Alsubih; Roquia Salam; Mohd Ahmed; Nabil Ben Kahla; Shamimuzzaman. 2020. "Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis." Theoretical and Applied Climatology 143, no. 1-2: 823-841.

Journal article
Published: 28 September 2020 in Ecological Indicators
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Hydrological variability (HA) in the river due to damming is well explored but there is a dearth of research about the HA on the riparian wetland. Time series data scarcity perhaps withstands against such research. The present work intended to measure the degree of HA of the river using flow data of gauge station and riparian wetland using time-series remote sensing data derived wetland indices as a proxy of water depth. For the first time, it is attempted to measure the degree of HA of wetland at pixel levels multi-analytical approaches like image-based hydrological attributes integration (HAI) approach, Histogram comparison approach (HCA), and Range of variability approach (RVA). The result has demonstrated that both river and wetlands have experienced significant HA after damming over the Atreyee river basin of India and Bangladesh. The degree of HA of the river in pre-monsoon and post-monsoon seasons respectively are 35.73% and 20.90% as per HCA and 27% and 28% as per RVA. In the wetland, this degree is quite higher (>66%) as per HCA and <33% as per RVA over 68% to 84% area. Image centric HAI shows that 14.85% and 22.75% hydrologically rich wetland area is converted lower hydrological zones for NDWI and MNDWI indices. Remote sensing data based HA analysis is successful and can be used in other environments. This HA in consequence of the dam on river and wetland may lead to a reverse impact on wetland habitat and ecosystem. Maintenance of ecological flow is necessary to make the situation ecologically relevant.

ACS Style

Swades Pal; Rajesh Sarda. Measuring the degree of hydrological variability of riparian wetland using hydrological attributes integration (HAI) histogram comparison approach (HCA) and range of variability approach (RVA). Ecological Indicators 2020, 120, 106966 .

AMA Style

Swades Pal, Rajesh Sarda. Measuring the degree of hydrological variability of riparian wetland using hydrological attributes integration (HAI) histogram comparison approach (HCA) and range of variability approach (RVA). Ecological Indicators. 2020; 120 ():106966.

Chicago/Turabian Style

Swades Pal; Rajesh Sarda. 2020. "Measuring the degree of hydrological variability of riparian wetland using hydrological attributes integration (HAI) histogram comparison approach (HCA) and range of variability approach (RVA)." Ecological Indicators 120, no. : 106966.

Journal article
Published: 12 September 2020 in Ecological Indicators
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The present study aims to measure wetland habitat vulnerability (WHV) in moribund deltaic part of India using ten conditioning parameters e.g., WPF, water depth, change in WPF, change in depth, APF, seasonality, fragmentation, distance from river, distance from road, and settlement area. For developing suitable models integrating the data layers, four bivariate models namely, logistic regression (LR) frequency ratio (FR), Shannon entropy (SE), and weights of evidence (WoE), and four machine learning (ML) algorithms namely, artificial neural network (ANN), J48 decision trees (DTs), random forest (RF), and reduced error pruning (REP) tree have been used. Results reveal that the 20 km2 (11.13%) to 35.70 km2 (19.88%) and 18.43 km2 (10.27%) to 29.01 km2 (16.16%) of area to total wetland area has emerged as high and very high habitat vulnerable zones in phase II, whereas, 17.60 km2 (11.72%) to 31.10 km2 (20.75%) and 16.23 km2 (10.82%) to 28.61 km2 (19.07%) of area found as high to very high vulnerable in phase III in case of both bivariate and ML models. Accuracy assessment of using AUC (ROC), Kappa, and overall accuracy have confirmed the acceptability of all the models but result of machine learning based model is more precise than bivariate models. Frequency ratio from bivariate models and REP tree from machine learning models are found to be the most acceptable. Sensitivity analysis shows that WPF factor followed by depth of wetland are the most important contributing factors. So, application of machine learning model for vulnerability study is recommended.

ACS Style

Swades Pal; Satyajit Paul. Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms. Ecological Indicators 2020, 119, 106866 .

AMA Style

Swades Pal, Satyajit Paul. Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms. Ecological Indicators. 2020; 119 ():106866.

Chicago/Turabian Style

Swades Pal; Satyajit Paul. 2020. "Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms." Ecological Indicators 119, no. : 106866.

Journal article
Published: 17 August 2020 in Ecological Indicators
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River island (locally known as charland) in river Ganges from Rajmahal hill to Farakka barrage of India is now under human habitat dominated by the environmentally evicted people triggered by bank erosion, but these are under different physical vulnerability like bank erosion, flooding etc. Considering the physical inconvenience, and inaccessibility, the present work intended to model the livelihood vulnerability state (LVS) by using advance machine learning algorithms, like Artificial neural network (ANN), Random forest (RF), Random subspace (RS) and Support vector machine (SVM). For LVS modelling, field and remote sensing based 26 parameters were selected. We classified the parameters as exposure (11 parameters), sensitivity (4 parameters) and adaptive capacity (11 parameters). We modelled LVS for overall condition, exposure, adaptive capacity, and sensitivity. Application of these algorithms in this field is unique and its robustness and precision in result is highly satisfactory. LVS models clearly identified 39% to 53% of areas having high to very high vulnerability and these are located at the edge of the charlands. Among the models, SVM outperformed as per the result of accuracy assessment. Therefore, it can be treated as a representative algorithm for LVS modelling. Among the 26 parameters, bank erosion, unhygienic condition, and Below Poverty Level (BPL) household parameters were found as highly dominating based on the findings of information and gain ratio. The correlation with LVS and three individual models (exposure, adaptive capacity, and sensitivity) exhibited that the Exposure model was highly correlated (r = 0.87) with high statistical significance (0.001 level).

ACS Style

Pankaj Singha; Priyanka Das; Swapan Talukdar; Swades Pal. Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor, India. Ecological Indicators 2020, 119, 106825 .

AMA Style

Pankaj Singha, Priyanka Das, Swapan Talukdar, Swades Pal. Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor, India. Ecological Indicators. 2020; 119 ():106825.

Chicago/Turabian Style

Pankaj Singha; Priyanka Das; Swapan Talukdar; Swades Pal. 2020. "Modeling livelihood vulnerability in erosion and flooding induced river island in Ganges riparian corridor, India." Ecological Indicators 119, no. : 106825.