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The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin.
Matej Vojtek; Jana Vojteková; Quoc Bao Pham. GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia. ISPRS International Journal of Geo-Information 2021, 10, 578 .
AMA StyleMatej Vojtek, Jana Vojteková, Quoc Bao Pham. GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia. ISPRS International Journal of Geo-Information. 2021; 10 (9):578.
Chicago/Turabian StyleMatej Vojtek; Jana Vojteková; Quoc Bao Pham. 2021. "GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia." ISPRS International Journal of Geo-Information 10, no. 9: 578.
The aim of this study is to develop landslide susceptibility models for the northern part of the Bordj Bou Arreridj (BBA) region, Northeast Algeria, to reduce the physical degradation caused by landslides and, to inspect what is required to properly control it. A comprehensive landslide inventory and susceptibility assessment of this region are not available, even though this region is prone to frequent disruption by geological hazards, mainly landslides. To achieve this objective, an inventory map and 12 variables (including geomorphic, geological, hydrological and environmental factors) are created. The inventory dataset is divided to training dataset with 148 landslides (70%) and validation dataset with 64 landslides (30%). Then, 2 machine learning (ML) techniques are applied to learn the internal relationship between the target set (212 landslide locations) and the 12 variables as inputs. The used methods are Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Their performances are assessed through the receiver operating characteristic (ROC) curve, the standard error (Std. error), and the confidence interval (CI) at 95%. As the main results, RF and XGBoost models give identical predictive accuracy (AUC) of ≈ 90%. This indicates that the proposed procedure can be useful for handling and monitoring present and future landslides. In addition, the models proposed in this study will be useful for the continuous assessment of land degradation trends for this region. Therefore, presenting these models in the best possible way allows stakeholders to benefit from them to identify key areas that may be targeted for protection and restoration procedures to achieve Land Degradation Neutrality (LDN) goals by 2030.
Yacine Achour; Zahra Saidani; Rania Touati; Quoc Bao Pham; Subodh Chandra Pal; Firuza Mustafa; Fusun Balik Sanli. Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environmental Earth Sciences 2021, 80, 1 -20.
AMA StyleYacine Achour, Zahra Saidani, Rania Touati, Quoc Bao Pham, Subodh Chandra Pal, Firuza Mustafa, Fusun Balik Sanli. Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality. Environmental Earth Sciences. 2021; 80 (17):1-20.
Chicago/Turabian StyleYacine Achour; Zahra Saidani; Rania Touati; Quoc Bao Pham; Subodh Chandra Pal; Firuza Mustafa; Fusun Balik Sanli. 2021. "Assessing landslide susceptibility using a machine learning-based approach to achieving land degradation neutrality." Environmental Earth Sciences 80, no. 17: 1-20.
Romulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International 2021, 1 -28.
AMA StyleRomulus Costache, Alireza Arabameri, Hossein Moayedi, Quoc Bao Pham, M. Santosh, Hoang Nguyen, Manish Pandey, Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International. 2021; ():1-28.
Chicago/Turabian StyleRomulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. 2021. "Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree." Geocarto International , no. : 1-28.
Large-scale climatic circulation modulates the weather patterns around the world. Understanding the teleconnections between large-scale circulation and local hydro-climatological variables has been a major thrust area of hydro-climatology research. The large-scale circulation is often quantified in terms of sea surface temperature (SST) and sea-level pressure (SLP). In this paper, we investigate the potential of wavelet neural network (WNN) hybrid model to predict maximum monthly discharge of the Madarsoo watershed, North of Iran considering two large-scale climatic signals like SST and SLP as inputs. Error measures like root-mean-square error (RMSE), and mean absolute error along with the correlation measures like coefficient of correlation (R), and Nash–Sutcliffe coefficient (CNS) were used to quantify the performance of prediction of maximum monthly discharge of three different hydrometry stations of the watershed. In all the cases, the WNN hybrid machine learning model was found to be giving superior performance consistently against the standalone artificial neural network (ANN) model and multiple linear regression model to predict the flood discharges of March and August months. The prediction of flood for August which is more devastating is found to be slightly better than the prediction of floods of March, in the stations served with smaller drainage area. The RMSE, R and CNS of Tamer hydrometry station in August were found to be 0.68, 0.996, and 0.99 m3/s, respectively, for the test period by using WNN model against 1.55, 0.989 and 0.95 by ANN model. Moreover, when evaluated for predicting the maximum monthly discharge in March and August between 2012 and 2013, the wavelet-based neural networks performed remarkably well than the ANN.
Nguyen Thi Thuy Linh; Hossein Ruigar; Saeed Golian; Getnet Taye Bawoke; Vivek Gupta; Khalil Ur Rahman; Adarsh Sankaran; Quoc Bao Pham. Flood prediction based on climatic signals using wavelet neural network. Acta Geophysica 2021, 1 -14.
AMA StyleNguyen Thi Thuy Linh, Hossein Ruigar, Saeed Golian, Getnet Taye Bawoke, Vivek Gupta, Khalil Ur Rahman, Adarsh Sankaran, Quoc Bao Pham. Flood prediction based on climatic signals using wavelet neural network. Acta Geophysica. 2021; ():1-14.
Chicago/Turabian StyleNguyen Thi Thuy Linh; Hossein Ruigar; Saeed Golian; Getnet Taye Bawoke; Vivek Gupta; Khalil Ur Rahman; Adarsh Sankaran; Quoc Bao Pham. 2021. "Flood prediction based on climatic signals using wavelet neural network." Acta Geophysica , no. : 1-14.
The study aims to evaluate the long-term accuracy of global precipitation (Climate Prediction Center (CPC) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR)) along with rain gauge at multiple temporal scales at the Vu Gia Thu Bon, Vietnam. Since there are few rainfall stations in this basin, it is important to validate multisources data for multiple purposes. It is the first time that lumped hydrological model i.e., Probability Distributed Moisture (PDM) is used for this basin. Various statistical indicators including correlation coefficient,, mean absolute error (MAE), root mean square error (RMSE), percent bias (BIAS) and Taylor diagram were used to evaluate the applicability of the global precipitation data and the PDM model. The results show that these precipitation data sets showed a good correlation with rain gauge rainfall data. Besides, CPC underestimates, while PERSIANN-CDR overestimates the rain gauge rainfall. In general, PERSIANN-CDR performed slightly better than CPC. The daily streamflow simulation driven by PDM and all data sources underestimates the actual flow.
Abro Mohammad Ilyas; Quoc Bao Pham; Dehua Zhu; Ehsan Elahi; Nguyen Thi Thuy Linh; Duong Tran Anh; Khaled Mohamed Khedher; Mohammad Ahmadlou. Multi sources hydrological assessment over Vu Gia Thu Bon Basin, Vietnam. Hydrological Sciences Journal 2021, 66, 1383 -1392.
AMA StyleAbro Mohammad Ilyas, Quoc Bao Pham, Dehua Zhu, Ehsan Elahi, Nguyen Thi Thuy Linh, Duong Tran Anh, Khaled Mohamed Khedher, Mohammad Ahmadlou. Multi sources hydrological assessment over Vu Gia Thu Bon Basin, Vietnam. Hydrological Sciences Journal. 2021; 66 (8):1383-1392.
Chicago/Turabian StyleAbro Mohammad Ilyas; Quoc Bao Pham; Dehua Zhu; Ehsan Elahi; Nguyen Thi Thuy Linh; Duong Tran Anh; Khaled Mohamed Khedher; Mohammad Ahmadlou. 2021. "Multi sources hydrological assessment over Vu Gia Thu Bon Basin, Vietnam." Hydrological Sciences Journal 66, no. 8: 1383-1392.
Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm−2.day−1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm−2.day−1) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm−2.day−1), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm−2.day−1), Angstrom-Prescott model (RMSE = 310 J.cm−2.day−1) and SVM (RMSE = 312 J.cm−2.day−1) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs of 382.81, 320.82 and 414.1 J.cm−2.day−1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.
Babak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal 2021, 1 .
AMA StyleBabak Mohammadi, Roozbeh Moazenzadeh, Quoc Bao Pham, Nadhir Al-Ansari, Khalil Ur Rahman, Duong Tran Anh, Zheng Duan. Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation. Ain Shams Engineering Journal. 2021; ():1.
Chicago/Turabian StyleBabak Mohammadi; Roozbeh Moazenzadeh; Quoc Bao Pham; Nadhir Al-Ansari; Khalil Ur Rahman; Duong Tran Anh; Zheng Duan. 2021. "Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation." Ain Shams Engineering Journal , no. : 1.
Water scarcity is a major challenge around the world, particularly in Ekpoma community, Edo State, Nigeria. The population depends on water vendors and reservoir tanks as a means of water supply. This study aims to make an assessment of groundwater potentials for effective and sustainable water resources management in Ekpoma. Seven criteria were considered to determine groundwater potentiality including slope, rainfall, land use, drainage density, distance to lineament, soil, and geology. According to their impact on groundwater, the parameters were grouped into fuzzy membership categories. The groundwater potentiality map was generated by overlaying the fuzzy members. Of the 101.2 km2 area of Ekpoma, the high, medium, and low potential zones cover 7.9, 6.4, and 85.7% of the total area, respectively. High and medium groundwater zones were identified mostly on the outskirt of the built-up areas. These groundwater potential areas were discovered to be predominant around the lineament areas suggesting that lineament plays a major role in the potential for groundwater in the study area. Reservoirs can be assigned in these high potential areas. Conclusively, the generated groundwater prospective map can be exploited for hydrological policy making and also by water supply engineers to predict the availability of groundwater.
Fidelis Odedishemi Ajibade; Olabanji Olatona Olajire; Temitope Fausat Ajibade; Olaolu George Fadugba; Temitope Ezekiel Idowu; Bashir Adelodun; Omobolaji Taofeek Opafola; Kayode Hassan Lasisi; James Rotimi Adewumi; Quoc Bao Pham. Groundwater potential assessment as a preliminary step to solving water scarcity challenges in Ekpoma, Edo State, Nigeria. Acta Geophysica 2021, 1 -15.
AMA StyleFidelis Odedishemi Ajibade, Olabanji Olatona Olajire, Temitope Fausat Ajibade, Olaolu George Fadugba, Temitope Ezekiel Idowu, Bashir Adelodun, Omobolaji Taofeek Opafola, Kayode Hassan Lasisi, James Rotimi Adewumi, Quoc Bao Pham. Groundwater potential assessment as a preliminary step to solving water scarcity challenges in Ekpoma, Edo State, Nigeria. Acta Geophysica. 2021; ():1-15.
Chicago/Turabian StyleFidelis Odedishemi Ajibade; Olabanji Olatona Olajire; Temitope Fausat Ajibade; Olaolu George Fadugba; Temitope Ezekiel Idowu; Bashir Adelodun; Omobolaji Taofeek Opafola; Kayode Hassan Lasisi; James Rotimi Adewumi; Quoc Bao Pham. 2021. "Groundwater potential assessment as a preliminary step to solving water scarcity challenges in Ekpoma, Edo State, Nigeria." Acta Geophysica , no. : 1-15.
Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.
Aqil Tariq; Hong Shu; Alban Kuriqi; Saima Siddiqui; Alexandre Gagnon; Linlin Lu; Nguyen Linh; Quoc Pham. Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images. Remote Sensing 2021, 13, 2053 .
AMA StyleAqil Tariq, Hong Shu, Alban Kuriqi, Saima Siddiqui, Alexandre Gagnon, Linlin Lu, Nguyen Linh, Quoc Pham. Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images. Remote Sensing. 2021; 13 (11):2053.
Chicago/Turabian StyleAqil Tariq; Hong Shu; Alban Kuriqi; Saima Siddiqui; Alexandre Gagnon; Linlin Lu; Nguyen Linh; Quoc Pham. 2021. "Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images." Remote Sensing 13, no. 11: 2053.
Historical exploration of flash flood events and producing flash flood susceptibility maps are crucial steps for decision makers in disaster management. In this paper, classification and regression tree (CART) methodology and its ensemble models of random forest (RF), boosted regression trees (BRT), and extreme gradient boosting (XGBoost) were implemented to create a flash flood susceptibility map of the Bâsca Chiojdului River Basin, one of the areas in Romania that is constantly exposed to flash floods. The torrential areas including 962 flash flood events were delineated from orthophotomaps and field observations. Furthermore, a set of conditioning forces to explain the flash floods was constructed which included aspect, land use and land cover (LULC), hydrological soil groups lithology, slope, topographic wetness index (TWI), topographic position index (TPI), profile curvature, convergence index, and stream power index (SPI). All models indicated the slope as the most important factor triggering the flash flood occurrence. The highest area under the curve (AUC) was achieved by the RF model (AUC =0.956), followed by the BRT model (AUC =0.899), XGBoost model (AUC =0.892), and CART model (AUC =0.868), respectively. The results showed that the central part of the Bâsca Chiojdului river basin, which covers approximately 30% of the study area, is more susceptible to flash flooding.
Rahebeh Abedi; Romulus Costache; Hossein Shafizadeh-Moghadam; Quoc Bao Pham. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International 2021, 1 -18.
AMA StyleRahebeh Abedi, Romulus Costache, Hossein Shafizadeh-Moghadam, Quoc Bao Pham. Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International. 2021; ():1-18.
Chicago/Turabian StyleRahebeh Abedi; Romulus Costache; Hossein Shafizadeh-Moghadam; Quoc Bao Pham. 2021. "Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees." Geocarto International , no. : 1-18.
Flooding is one of the most frequently occurring natural hazards worldwide. Mapping and assessment of possible flood hazards are critical components of the evaluation and mitigation of flood risk. In this study, six flood-related indices, i.e., slope, elevation, distance to discharge channel, runoff volume, street-drainage network intersection, index of the development and persistence of the drainage network (IDPR), were used to assess the flood hazard. The entropy weighting method was used for assigning the weights to flood-related indices and combining them to prepare urban flood hazard mapping in Hamadan city. The produced map showed that nearly 20% of the study area (14.7 km2) corresponded to very high susceptibility to flooding, 19.4% (143 km2) to high susceptibility and 20.3%, 20.7% and 19.6% regard the moderate, low and very low susceptibility to flooding, respectively. Finally, two methods were used to evaluate the accuracy of the produced flood susceptibility map. The first method is related to assessing the behavior of the map by making and propagating error in flood-related indices and used model (entropy weighting method), and the second method is superimposing method. The results showed that by making and propagation of error, the behavior of producing flood susceptibility mapping, the produced map has a robust behavior either in ranking importance of flood-related indices and percentage of flood susceptibility areas. On the other hand, regarding the result of the superimposing method, the accuracy of the flood susceptibility map was 72%, which also suggests an acceptable result.
Hossein Malekinezhad; Mehdi Sepehri; Quoc Bao Pham; Seyed Zeynalabedin Hosseini; Sarita Gajbhiye Meshram; Matej Vojtek; Jana Vojteková. Application of entropy weighting method for urban flood hazard mapping. Acta Geophysica 2021, 69, 841 -854.
AMA StyleHossein Malekinezhad, Mehdi Sepehri, Quoc Bao Pham, Seyed Zeynalabedin Hosseini, Sarita Gajbhiye Meshram, Matej Vojtek, Jana Vojteková. Application of entropy weighting method for urban flood hazard mapping. Acta Geophysica. 2021; 69 (3):841-854.
Chicago/Turabian StyleHossein Malekinezhad; Mehdi Sepehri; Quoc Bao Pham; Seyed Zeynalabedin Hosseini; Sarita Gajbhiye Meshram; Matej Vojtek; Jana Vojteková. 2021. "Application of entropy weighting method for urban flood hazard mapping." Acta Geophysica 69, no. 3: 841-854.
Magnitude frequency analysis of suspended sediment transport provides important information on the sediment transport characteristic of a river. Understanding the sediment transport characteristic of rivers plays a vital role in the management of water resource projects. The lower Drava River Basin is one of the most extensively hydroelectrically exploited river basins in the world. In this regard, the analysis of sediment transport characteristic in the region is critical. In the present study, magnitude frequency analysis was performed for the Botovo and Donji Miholjac gauging stations on the lower Drava River. It was observed that discharges close to average daily discharge are responsible for transporting major fraction of suspended sediment at both the stations. The effective discharge was found to be less than half of Q1.5 and Q2. It was also observed that data aggregation affects the effective discharge. Estimation of factor load discharge reveals that discharge of return interval around one year on annual maximum discharge time series transport 90% of the total sediment load in the lower Drava River.
Mohammad Zakwan; Quoc Bao Pham; Senlin Zhu. Effective discharge computation in the lower Drava River. Hydrological Sciences Journal 2021, 66, 826 -837.
AMA StyleMohammad Zakwan, Quoc Bao Pham, Senlin Zhu. Effective discharge computation in the lower Drava River. Hydrological Sciences Journal. 2021; 66 (5):826-837.
Chicago/Turabian StyleMohammad Zakwan; Quoc Bao Pham; Senlin Zhu. 2021. "Effective discharge computation in the lower Drava River." Hydrological Sciences Journal 66, no. 5: 826-837.
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Tirthankar Basu; Arijit Das; Quoc Bao Pham; Nadhir Al-Ansari; Nguyen Thi Thuy Linh; Gareth Lagerwall. Author Correction: Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India. Scientific Reports 2021, 11, 1 -1.
AMA StyleTirthankar Basu, Arijit Das, Quoc Bao Pham, Nadhir Al-Ansari, Nguyen Thi Thuy Linh, Gareth Lagerwall. Author Correction: Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India. Scientific Reports. 2021; 11 (1):1-1.
Chicago/Turabian StyleTirthankar Basu; Arijit Das; Quoc Bao Pham; Nadhir Al-Ansari; Nguyen Thi Thuy Linh; Gareth Lagerwall. 2021. "Author Correction: Development of an integrated peri-urban wetland degradation assessment approach for the Chatra Wetland in eastern India." Scientific Reports 11, no. 1: 1-1.
Abu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh. Environmental Science and Pollution Research 2021, 1 -1.
AMA StyleAbu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, Shumona Akhter, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, Nguyen Thi Thuy Linh. Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh. Environmental Science and Pollution Research. 2021; ():1-1.
Chicago/Turabian StyleAbu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. 2021. "Correction to: Machine learning algorithm-based risk assessment of riparian wetlands in Padma River basin of Northwest Bangladesh." Environmental Science and Pollution Research , no. : 1-1.
Wetland risk assessment is a global concern especially in developing countries like Bangladesh. The present study explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment. The wetland risk assessment was predicted based on ten selected parameters, such as fragmentation probability, distance to road, and settlement. We used M5P, random forest (RF), reduced error pruning tree (REPTree), and support vector machine (SVM) machine learning techniques for wetland risk assessment. The results showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River. The distance to the river and built-up area are the two most contributing drivers influencing the wetland risk assessment based on information gain ratio (InGR). The prediction results of machine learning models showed 64.48% of area by M5P, 61.75% of area by RF, 62.18% of area by REPTree, and 55.74% of area by SVM have been predicted as the high and very high-risk zones. The results of accuracy assessment showed that the RF outperformed than other models (area under curve: 0.83), followed by the SVM, M5P, and REPTree. Degradation of wetlands explored in this study demonstrated the negative effects on biodiversity. Therefore, to conserve and protect the wetlands, continuous monitoring of wetlands using high resolution satellite images, feeding with the ecological flow, confining built up area and agricultural expansion towards wetlands, and new wetland creation is essential for wetland management.
Abu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environmental Science and Pollution Research 2021, 1 -22.
AMA StyleAbu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sk Ziaul, Kutub Uddin Eibek, Shumona Akhter, Quoc Bao Pham, Babak Mohammadi, Firoozeh Karimi, Nguyen Thi Thuy Linh. Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh. Environmental Science and Pollution Research. 2021; ():1-22.
Chicago/Turabian StyleAbu Reza Md. Towfiqul Islam; Swapan Talukdar; Susanta Mahato; Sk Ziaul; Kutub Uddin Eibek; Shumona Akhter; Quoc Bao Pham; Babak Mohammadi; Firoozeh Karimi; Nguyen Thi Thuy Linh. 2021. "Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh." Environmental Science and Pollution Research , no. : 1-22.
It is a known fact that the size, frequency, and spatial variability of hydrometeorological variables will irregularly increase under the impact of climate change. Among the hydrometeorological variables, rainfall is one of the most important. Trend analysis is one of the most effective methods of observing the effects of climate change on rainfall. Recently, new graphical methods have been proposed as an alternative to classical trend analysis methods. Innovative Polygon Trend Analysis (IPTA), which evolved from Innovative Trend Analysis (ITA), is currently one of the proposed methods and it does not contain any assumptions. The aim of this study is to compare IPTA, ITA with the Significance Test and Mann-Kendall (MK) methods. To achieve this, the monthly total rainfall trends of 15 stations in the Vu Gia-Thu Bon River Basin (VGTBRB) of Vietnam have been examined for the period 1979–2016. The analyses show that rainfall tends to increase (decrease) in March (June) at nearly all stations. IPTA and ITA with the Significance Test are more sensitive than MK in determining the trends. While trends were detected in approximately 90% of all months in IPTA and ITA with the Significance Test, this rate was only 23% in the MK test. Although the arithmetic mean graphs in the 1-year hydrometeorological cycle are considerably regular at almost all stations, their standard deviations are relatively irregular. The most critical month for trend transitions between consecutive months for all the stations is October, which has an average trend slope of −1.35 and a trend slope ranging from −3.98 to −0.21, which shows a decreasing trend.
Murat Şan; Fatma Akçay; Nguyen Thi Thuy Linh; Murat Kankal; Quoc Bao Pham. Innovative and polygonal trend analyses applications for rainfall data in Vietnam. Theoretical and Applied Climatology 2021, 144, 809 -822.
AMA StyleMurat Şan, Fatma Akçay, Nguyen Thi Thuy Linh, Murat Kankal, Quoc Bao Pham. Innovative and polygonal trend analyses applications for rainfall data in Vietnam. Theoretical and Applied Climatology. 2021; 144 (3-4):809-822.
Chicago/Turabian StyleMurat Şan; Fatma Akçay; Nguyen Thi Thuy Linh; Murat Kankal; Quoc Bao Pham. 2021. "Innovative and polygonal trend analyses applications for rainfall data in Vietnam." Theoretical and Applied Climatology 144, no. 3-4: 809-822.
Determining areas of high groundwater potential is important for exploitation, management, and protection of water resources. This study assesses the spatial distribution of groundwater potential in the Zarrineh Rood watershed of Kurdistan Province, Iran using combinations of five statistical and machine learning algorithms – frequency ratio (FR), radial basis function (RBF), index of entropy (IOE), evidential belief function (EBF) and fuzzy art map (FAM). To accomplish this, 1448 well locations in the study area were randomly divided into two data sets for training (70%= 1013 locations) and validation (30%= 435 locations) based on the holdout method. Fourteen factors that can affect the presence or absence of groundwater were identified, measured, and mapped using ArcGIS and SAGA-GIS software. The models were used to predict the locations of groundwater based on suitable combinations of the conditioning factors to produce groundwater potential maps. The probability of groundwater at any location was classified as low, moderate, high, or very high based on natural breaks in the data spectrum. The model predictions were tested for validity and their success was determined using receiver operating characteristic (ROC) curves, standard errors (SE), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF) and accuracy (ACC), and the Friedman test. The performance assessments of groundwater potential predictions using the area under the curve (AUC) and accuracy (ACC) showed that the FR-RBF model had very good performance (AUC= 0.889, ACC= 87.51). And FR-FAM (AUC= 0.869, ACC= 84.67), EBF-FAM (AUC= 0.864, ACC= 84.42), EBF-RBF (AUC= 0.854, ACC= 83.94), FR-IOE (AUC= 0.836, ACC= 83.62), and EBF-IOE (AUC= 0.833, ACC= 80.42) also had acceptable performance. The results of the Friedman test also show that there are significant differences between the models and the highest mean rank was generated by the FR-FAM model (3.642). Therefore, the hybrid models can be used to increase the accuracy of groundwater-prediction models in the study region and perhaps in similar settings.
Peyman Yariyan; Mohammadtaghi Avand; Ebrahim Omidvar; Quoc Bao Pham; Nguyen Thi Thuy Linh; John P. Tiefenbacher. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto International 2021, 1 -35.
AMA StylePeyman Yariyan, Mohammadtaghi Avand, Ebrahim Omidvar, Quoc Bao Pham, Nguyen Thi Thuy Linh, John P. Tiefenbacher. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto International. 2021; ():1-35.
Chicago/Turabian StylePeyman Yariyan; Mohammadtaghi Avand; Ebrahim Omidvar; Quoc Bao Pham; Nguyen Thi Thuy Linh; John P. Tiefenbacher. 2021. "Optimization of statistical and machine learning hybrid models for groundwater potential mapping." Geocarto International , no. : 1-35.
Digital surface models, land use and rainfall data were used to simulate water areas using Hydrologic Engineering Centres River Analysis System (HEC‐RAS) software. Multi‐temporal synthetic aperture radar (SAR) was used for the detection of flood prone area to calibrate HEC‐RAS, due to the lack of data validation in the New Cairo City, Egypt. The thresholding water detection method was applied to two Sentinel‐1 images, one pre‐ and one post‐flash flood event from April 24 to 27, 2018. The threshold method was used to detect water areas from SAR Sentinel‐1 images. Feature statistical agreement F1 and F2 values ranged from 73.4 to 77.7% between water areas extracted based on backscattering values between 19.97 and 16.53 in decibels (dB) and reference water areas obtained using an optical image of the Sentinel‐2 satellite. The similarity between simulated HEC‐RAS two‐dimensional (2D) of water areas and reference water areas based on SAR data ranged between 74.2 and 89.7% using feature statistical agreement values F1 and F2. It provides a clear suggestion that, in the absence of field observations, SAR data can be used to calibrate the model. Two flood hazard maps created based on water velocity and depth were obtained from HEC‐RAS 2D simulation. The obtained maps indicated that 11% of the roads and 50% of the buildings in New Cairo City are exposed to high hazard areas. Furthermore, 28% of the bare land is situated in a very high vulnerability area. We recommend the use of obtained hazard map in supporting emergency response, and designing effective emergency plans.
Ismail Elkhrachy; Quoc Bao Pham; Romulus Costache; Meriame Mohajane; Khalil Ur Rahman; Himan Shahabi; Nguyen Thi Thuy Linh; Duong Tran Anh. Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. Journal of Flood Risk Management 2021, 14, e12692 .
AMA StyleIsmail Elkhrachy, Quoc Bao Pham, Romulus Costache, Meriame Mohajane, Khalil Ur Rahman, Himan Shahabi, Nguyen Thi Thuy Linh, Duong Tran Anh. Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt. Journal of Flood Risk Management. 2021; 14 (2):e12692.
Chicago/Turabian StyleIsmail Elkhrachy; Quoc Bao Pham; Romulus Costache; Meriame Mohajane; Khalil Ur Rahman; Himan Shahabi; Nguyen Thi Thuy Linh; Duong Tran Anh. 2021. "Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt." Journal of Flood Risk Management 14, no. 2: e12692.
There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.
Romulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors 2021, 21, 280 .
AMA StyleRomulus Costache, Alireza Arabameri, Thomas Blaschke, Quoc Pham, Binh Pham, Manish Pandey, Aman Arora, Nguyen Linh, Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors. 2021; 21 (1):280.
Chicago/Turabian StyleRomulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. 2021. "Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors." Sensors 21, no. 1: 280.
Identification of areas susceptible to floods is an important issue which requires an increased attention due to the changing frequency and magnitude of floods, which is mainly a result of the ongoing climate change and increasing anthropic pressure on the landscape. In this study, the aim was to identify the areas susceptible to floods using and comparing two different approaches, namely the multi-criteria decision analysis-analytical hierarchy process (MCDA-AHP) and the machine learning-boosted classification (BCT) and boosted regression (BRT) tree. The study area was represented by the Topľa river basin, Slovakia. Altogether, seven relevant flood conditioning factors: elevation, slope, river network density, distance from river, flow accumulation, curve numbers and lithology as well as flood inventory database consisting of 107 flood locations were used. Based on the results, almost 40% of the study area is characterized by high to very high flood susceptibility using the MCDA-AHP. In case of the BCT and BRT models, the share of high and very high flood susceptibility class on the basin area is 45% and 38%, respectively. Validation of the performed flood susceptibility models confirmed generally higher accuracy of the machine learning models. The accuracy of the MCDA-AHP model was 81.33% while the accuracy of the boosted tree models was 87.70% and 91.42%, respectively, for classification and regression. The results of this study can enhance more effective preliminary flood risk assessment according to the EU Floods Directive.
Matej Vojtek; Jana Vojteková; Romulus Costache; Quoc Bao Pham; Sunmin Lee; Arfan Arshad; Satiprasad Sahoo; Nguyen Thi Thuy Linh; Duong Tran Anh. Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Geomatics, Natural Hazards and Risk 2021, 12, 1153 -1180.
AMA StyleMatej Vojtek, Jana Vojteková, Romulus Costache, Quoc Bao Pham, Sunmin Lee, Arfan Arshad, Satiprasad Sahoo, Nguyen Thi Thuy Linh, Duong Tran Anh. Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Geomatics, Natural Hazards and Risk. 2021; 12 (1):1153-1180.
Chicago/Turabian StyleMatej Vojtek; Jana Vojteková; Romulus Costache; Quoc Bao Pham; Sunmin Lee; Arfan Arshad; Satiprasad Sahoo; Nguyen Thi Thuy Linh; Duong Tran Anh. 2021. "Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia." Geomatics, Natural Hazards and Risk 12, no. 1: 1153-1180.
The strict lockdown measures not only contributed to curbing the spread of COVID-19 infection, but also improved the environmental conditions worldwide. The main goal of the current study was to investigate the co-benefits of COVID-19 lockdown on the atmosphere and aquatic ecological system under restricted anthropogenic activities in South Asia. The remote sensing data (a) NO2 emissions from the Ozone Monitoring Instrument (OMI), (b) Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and (c) chlorophyll (Chl-a) and turbidity data from MODIS-Aqua Level-3 during Jan–Oct (2020) were analyzed to assess the changes in air and water pollution compared to the last five years (2015–2019). The interactions between the air and water pollution were also investigated using overland runoff and precipitation in 2019 and 2020 at a monthly scale to investigate the anomalous events, which could affect the N loading to coastal regions. The results revealed a considerable drop in the air and water pollution (30–40% reduction in NO2 emissions, 45% in AOD, 50% decline in coastal Chl-a concentration, and 29% decline in turbidity) over South Asia. The rate of reduction in NO2 emissions was found the highest for Lahore (32%), New Delhi (31%), Ahmadabad (29%), Karachi (26%), Hyderabad (24%), and Chennai (17%) during the strict lockdown period from Apr–Jun, 2020. A positive correlation between AOD and NO2 emissions (0.23–0.50) implies that a decrease in AOD is attributed to a reduction in NO2. It was observed that during strict lockdown, the turbidity has decreased by 29%, 11%, 16%, and 17% along the coastal regions of Karachi, Mumbai, Calcutta, and Dhaka, respectively, while a 5–6% increase in turbidity was seen over the Madras during the same period. The findings stress the importance of reduced N emissions due to halted fossil fuel consumption and their relationships with the reduced air and water pollution. It is concluded that the atmospheric and hydrospheric environment can be improved by implementing smart restrictions on fossil fuel consumption with a minimum effect on socioeconomics in the region. Smart constraints on fossil fuel usage are recommended to control air and water pollution even after the social and economic activities resume business-as-usual scenario.
Muhammad Shafeeque; Arfan Arshad; Ahmed Elbeltagi; Abid Sarwar; Quoc Bao Pham; Shahbaz Nasir Khan; Adil Dilawar; Nadhir Al-Ansari. Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia. Geomatics, Natural Hazards and Risk 2021, 12, 560 -580.
AMA StyleMuhammad Shafeeque, Arfan Arshad, Ahmed Elbeltagi, Abid Sarwar, Quoc Bao Pham, Shahbaz Nasir Khan, Adil Dilawar, Nadhir Al-Ansari. Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia. Geomatics, Natural Hazards and Risk. 2021; 12 (1):560-580.
Chicago/Turabian StyleMuhammad Shafeeque; Arfan Arshad; Ahmed Elbeltagi; Abid Sarwar; Quoc Bao Pham; Shahbaz Nasir Khan; Adil Dilawar; Nadhir Al-Ansari. 2021. "Understanding temporary reduction in atmospheric pollution and its impacts on coastal aquatic system during COVID-19 lockdown: a case study of South Asia." Geomatics, Natural Hazards and Risk 12, no. 1: 560-580.