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I am an Associate Prof. in Watershed Management Engineering and Sciences; Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran. my web page: http://sess.shirazu.ac.ir/sess/FResearch/FacultyCV.aspx?OP=Code=271143;Lng=En;
In recent years, gully erosion has ceased many development activities and imposed a living threat to local communities residing in southern Iran. Hence, this study sets out to investigate the prediction performance of a machine learning model named the quick, unbiased, efficient statistical tree (QUEST) model for gully susceptibility mapping. Its results were compared to two conventional statistical models: frequency ratio (FR) and evidential belief function (EBF). The area under the receiver operating characteristic (AUROC) and the true skill statistic (TSS) metrics were adopted to assess models' goodness-of-fit and predictive performance in the corresponding training and validation stages. Results revealed that the QUEST model outperforms its counterparts by giving respective AUROC and TSS values of 88.5% and 0.77 in the training stage, followed by EBF (82.3% and 0.65) and FR (80.4% and 0.62). Similarly, the QUEST model showed the highest AUROC and TSS values in the validation stage (83.2% and 0.63, respectively), followed by the EBF (78.6% and 0.63, respectively) and FR (77.1% and 0.58, respectively). Further scrutinization attested that the QUEST model offers a more practical, compendious, and adaptable susceptibility map based on which about 32% of the study area was identified as the high susceptibility zone to gully erosion. Hence, highly gully susceptible areas require pragmatic mitigation plans. In addition, the application of machine learning models for gully erosion merits further studies.
Seyed Masoud Soleimanpour; Hamid Reza Pourghasemi; Maryam Zare. A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models. CATENA 2021, 207, 105679 .
AMA StyleSeyed Masoud Soleimanpour, Hamid Reza Pourghasemi, Maryam Zare. A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models. CATENA. 2021; 207 ():105679.
Chicago/Turabian StyleSeyed Masoud Soleimanpour; Hamid Reza Pourghasemi; Maryam Zare. 2021. "A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models." CATENA 207, no. : 105679.
The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low = −12.04 %, Low = −8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low = −14.48 %, Low = −6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.
Saeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management 2021, 298, 113551 .
AMA StyleSaeid Janizadeh, Subodh Chandra Pal, Asish Saha, Indrajit Chowdhuri, Kourosh Ahmadi, Sajjad Mirzaei, Amir Hossein Mosavi, John P. Tiefenbacher. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. Journal of Environmental Management. 2021; 298 ():113551.
Chicago/Turabian StyleSaeid Janizadeh; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Kourosh Ahmadi; Sajjad Mirzaei; Amir Hossein Mosavi; John P. Tiefenbacher. 2021. "Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future." Journal of Environmental Management 298, no. : 113551.
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
Seyed Vahid Razavi-Termeh; Abolghasem Sadeghi-Niaraki; Soo-Mi Choi. Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms. Remote Sensing 2021, 13, 3222 .
AMA StyleSeyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi. Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms. Remote Sensing. 2021; 13 (16):3222.
Chicago/Turabian StyleSeyed Vahid Razavi-Termeh; Abolghasem Sadeghi-Niaraki; Soo-Mi Choi. 2021. "Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms." Remote Sensing 13, no. 16: 3222.
This study applied to evaluate landslide susceptibility using four data mining models including, “Generalized Linear Model (GLM)”, “Maximum Entropy (ME)”, “Artificial Neural Network (ANN)”, and “Support Vector Machine (SVM)” in Cherikabad Watershed in Urmia City, Iran. In particular, Shannon entropy was used to assess the intercomparison of factors’ classes. Eleven factors including, elevation, slope angle, slope aspect, geological formation, annual mean rainfall, land use/ land cover, distance to the village, distance to faults, distance to roads, distance to streams, and NDVI used in the current study. Landslide inventory map was identified using Google Earth imagery, extensive field surveys, and scrutinizing archived data. The produced landslide susceptibility maps were evaluated by the AUROC index. The results of performance metrics revealed that the Shannon entropy with an AUROC of 0.879 proved highly reliable and so is the intercomparison analysis of factors’ classes derived from it. Additionally, the goodness-of-fit of the GLM, ME, ANN, and SVM models were 0.763, 0.740, 0.926, and 0.924, while their predictive powers were 0.751, 0.727, 0.917, and 0.935, respectively. Hence, the results indicated that the SVM model can be introduced as the superior model for the study area based on which the most critical factors affecting landslides were found to be elevation, annual mean rainfall, and distance to the village. The results of this work are of great use for land use planning in landslide-prone areas with similar geo-topological, geomorphological, and climatic conditions.
Abdulaziz Hanifinia; Habib Nazarnejad; Saeed Najafi; Aiding Kornejady; Hamid Reza Pourghasemi. Landslide susceptibility assessment and mapping using statistical and data mining models in Iran. 2021, 1 .
AMA StyleAbdulaziz Hanifinia, Habib Nazarnejad, Saeed Najafi, Aiding Kornejady, Hamid Reza Pourghasemi. Landslide susceptibility assessment and mapping using statistical and data mining models in Iran. . 2021; ():1.
Chicago/Turabian StyleAbdulaziz Hanifinia; Habib Nazarnejad; Saeed Najafi; Aiding Kornejady; Hamid Reza Pourghasemi. 2021. "Landslide susceptibility assessment and mapping using statistical and data mining models in Iran." , no. : 1.
Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM2.5, respectively.
Seyedeh Zeinab Shogrkhodaei; Seyed Vahid Razavi-Termeh; Amanollah Fathnia. Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution 2021, 289, 117859 .
AMA StyleSeyedeh Zeinab Shogrkhodaei, Seyed Vahid Razavi-Termeh, Amanollah Fathnia. Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution. 2021; 289 ():117859.
Chicago/Turabian StyleSeyedeh Zeinab Shogrkhodaei; Seyed Vahid Razavi-Termeh; Amanollah Fathnia. 2021. "Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms." Environmental Pollution 289, no. : 117859.
Purushothaman C. Abhilash; Simone Bastianoni; Weiqiang Chen; Ruth DeFries; Leonardo F. Fraceto; Neven S. Fuckar; Shizuka Hashimoto; Danny Hunter; Saskia Keesstra; Othmane Merah; Patrick O’Farrell; Prajal Pradhan; Simron Singh; Pete Smith; Lindsay C. Stringer; B. L. Turner. Introducing ‘Anthropocene Science’: A New International Journal for Addressing Human Impact on the Resilience of Planet Earth. Anthropocene Science 2021, 1 -4.
AMA StylePurushothaman C. Abhilash, Simone Bastianoni, Weiqiang Chen, Ruth DeFries, Leonardo F. Fraceto, Neven S. Fuckar, Shizuka Hashimoto, Danny Hunter, Saskia Keesstra, Othmane Merah, Patrick O’Farrell, Prajal Pradhan, Simron Singh, Pete Smith, Lindsay C. Stringer, B. L. Turner. Introducing ‘Anthropocene Science’: A New International Journal for Addressing Human Impact on the Resilience of Planet Earth. Anthropocene Science. 2021; ():1-4.
Chicago/Turabian StylePurushothaman C. Abhilash; Simone Bastianoni; Weiqiang Chen; Ruth DeFries; Leonardo F. Fraceto; Neven S. Fuckar; Shizuka Hashimoto; Danny Hunter; Saskia Keesstra; Othmane Merah; Patrick O’Farrell; Prajal Pradhan; Simron Singh; Pete Smith; Lindsay C. Stringer; B. L. Turner. 2021. "Introducing ‘Anthropocene Science’: A New International Journal for Addressing Human Impact on the Resilience of Planet Earth." Anthropocene Science , no. : 1-4.
We used three state-of-the-art machine learning techniques (boosted regression tree, random forest, and support vector machine) to produce a multi-hazard (MHR) map illustrating areas susceptible to flooding, gully erosion, forest fires, and earthquakes in Kohgiluyeh and Boyer-Ahmad Province, Iran. The earthquake hazard map was derived from a probabilistic seismic hazard analysis. The mean decrease Gini (MDG) method was implemented to determine the relative importance of effective factors on the spatial occurrence of each of the four hazards. Area under the curve (AUC) plots, based on a validation dataset, were created for the maps generated using the three algorithms to compare the results. The random forest model had the highest predictive accuracy, with AUC values of 0.994, 0.982, and 0.885 for gully erosion, flooding, and forest fires, respectively. Approximately 41%, 40%, 28%, and 3% of the study area are at risk of forest fires, earthquakes, floods, and gully erosion, respectively.
Soheila Pouyan; Hamid Reza Pourghasemi; Mojgan Bordbar; Soroor Rahmanian; John J. Clague. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports 2021, 11, 1 .
AMA StyleSoheila Pouyan, Hamid Reza Pourghasemi, Mojgan Bordbar, Soroor Rahmanian, John J. Clague. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Scientific Reports. 2021; 11 ():1.
Chicago/Turabian StyleSoheila Pouyan; Hamid Reza Pourghasemi; Mojgan Bordbar; Soroor Rahmanian; John J. Clague. 2021. "A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran." Scientific Reports 11, no. : 1.
The importance of soils to society has gained increasing recognition over the past decade, with the potential to contribute to most of the United Nations’ Sustainable Development Goals (SDGs). With unprecedented and growing demands for food, water and energy, there is an urgent need for a global effort to address the challenges of climate change and land degradation, whilst protecting soil as a natural resource. In this paper, we identify the contribution of soil science over the past decade to addressing gaps in our knowledge regarding major environmental challenges: climate change, food security, water security, urban development, and ecosystem functioning and biodiversity. Continuing to address knowledge gaps in soil science is essential for the achievement of the SDGs. However, with limited time and budget, it is also pertinent to identify effective methods of working that ensure the research carried out leads to real-world impact. Here, we suggest three strategies for the next decade of soil science, comprising a greater implementation of research into policy, interdisciplinary partnerships to evaluate function trade-offs and synergies between soils and other environmental domains, and integrating monitoring and modelling methods to ensure soil-based policies can withstand the uncertainties of the future. Highlights We highlight the contributions of soil science to five major environmental challenges since 2010. Researchers have contributed to recommendation reports, but work is rarely translated into policy. Interdisciplinary work should assess trade-offs and synergies between soils and other domains. Integrating monitoring and modelling is key for robust and sustainable soils-based policymaking.
Daniel L. Evans; Victoria Janes‐Bassett; Pasquale Borrelli; Claire Chenu; Carla S. S. Ferreira; Robert I. Griffiths; Zahra Kalantari; Saskia Keesstra; Rattan Lal; Panos Panagos; David A. Robinson; Samaneh Seifollahi‐Aghmiuni; Pete Smith; Tammo S. Steenhuis; Amy Thomas; Saskia M. Visser. Sustainable futures over the next decade are rooted in soil science. European Journal of Soil Science 2021, 1 .
AMA StyleDaniel L. Evans, Victoria Janes‐Bassett, Pasquale Borrelli, Claire Chenu, Carla S. S. Ferreira, Robert I. Griffiths, Zahra Kalantari, Saskia Keesstra, Rattan Lal, Panos Panagos, David A. Robinson, Samaneh Seifollahi‐Aghmiuni, Pete Smith, Tammo S. Steenhuis, Amy Thomas, Saskia M. Visser. Sustainable futures over the next decade are rooted in soil science. European Journal of Soil Science. 2021; ():1.
Chicago/Turabian StyleDaniel L. Evans; Victoria Janes‐Bassett; Pasquale Borrelli; Claire Chenu; Carla S. S. Ferreira; Robert I. Griffiths; Zahra Kalantari; Saskia Keesstra; Rattan Lal; Panos Panagos; David A. Robinson; Samaneh Seifollahi‐Aghmiuni; Pete Smith; Tammo S. Steenhuis; Amy Thomas; Saskia M. Visser. 2021. "Sustainable futures over the next decade are rooted in soil science." European Journal of Soil Science , no. : 1.
Loess-derived soils in semi-arid regions are valuable resources. These regions have erosional landforms (e.g., piping, gully heads, and gullies) complexly produced by geo-environmental forces. Identification of the connections between landform patterns and the underlying geomorphological mechanisms is essential for understanding how erosive rainfall creates blind gully heads from pipes and gullies from gully heads. The goal of this study was to clarify the complex spatial interactions between geomorphological processes resulting from extremely intense extreme rainfall events. Fieldwork was conducted to map all collapsed pipes (single and multiple sinkholes), closed depressions, and gully heads using photogrammetric drones in 2018 and 2019 in a 2700-hectare area of loess-derived soils. In 2018, 837 pipes and 283 gully head locations were identified. In 2019, these numbered 1034 and 549. Geomorphic transformations were described statistically and the erosional landforms were compared to land-degradation trends. Piping and head-cut types are reflections of specific geomorphic conditions in the hilly loess topography. A conceptual model is proposed to characterize the landform shifts in loess deposits in four dynamic states. The spatial processes and interactions of collapsed pipes and gully heads reveal the natural processes underlying their formation and provide insights that my help to identify solutions to curtail destructive activities and mitigate the forces driving erosion in regions similar to the study area.
Narges Kariminejad; Mohsen Hosseinalizadeh; Hamid Reza Pourghasemi; John P. Tiefenbacher. Change detection in piping, gully head forms, and mechanisms. CATENA 2021, 206, 105550 .
AMA StyleNarges Kariminejad, Mohsen Hosseinalizadeh, Hamid Reza Pourghasemi, John P. Tiefenbacher. Change detection in piping, gully head forms, and mechanisms. CATENA. 2021; 206 ():105550.
Chicago/Turabian StyleNarges Kariminejad; Mohsen Hosseinalizadeh; Hamid Reza Pourghasemi; John P. Tiefenbacher. 2021. "Change detection in piping, gully head forms, and mechanisms." CATENA 206, no. : 105550.
Studying the earth's climatic and environmental system requires precise land cover (LC) information, particularly over forested areas to accurately account for the carbon budget. This study's objective was to evaluate the accuracy of the moderate resolution imaging spectroradiometer (MODIS) LC product (MCD12Q1) over a forested area covering Northern Iran. We applied fuzzy sets theory and quantified the magnitude, source, and nature of the errors in the MODIS landcover product. Compared to the traditional classical set theory, the fuzzy set theory was found to be more compatible with the continuum nature of land surface features, thus more suitable for LC identification and evaluation. Our accuracy assessment of MCD12Q1 products showed specific patterns of uncertainty and inconsistency when the product was compared against the finer resolution platforms (i.e., Landsat, Google Earth, and local observation). The overall accuracy of MODIS landcover was estimated at 68.3% considering all MCD12Q1 landcover classes and 89% considering the forest category. More specifically, the “dense forest” class was identified as the most accurate (~95% accuracy), whereas the “open forest” class was the least accurate (45% accuracy), and the “forest/cropland” class was the most challenging to analyze. The “natural herbaceous” class was repeatedly misidentified with the “herbaceous cropland” and “open forest” categories leading to the largest uncertainty compared to other LC classes (confusion value = 53). The applied method that is used in this study was most useful to illustrate the degree of accuracy of MODIS product with actual LC types. For the heterogeneous grid cell where several landcover types were present, the MODIS could not accurately detect the LC type, and high error, confusion, and ambiguity have resulted. This signified the inefficiency of MODIS's spatial resolution for LC assessment in sensitive applications (e.g., LC change detection).
Maryam Naghdizadegan Jahromi; Mojtaba Naghdyzadegan Jahromi; Hamid Reza Pourghasemi; Shahrokh Zand-Parsa; Sajad Jamshidi. Accuracy assessment of forest mapping in MODIS land cover dataset using fuzzy set theory. Forest Resources Resilience and Conflicts 2021, 165 -183.
AMA StyleMaryam Naghdizadegan Jahromi, Mojtaba Naghdyzadegan Jahromi, Hamid Reza Pourghasemi, Shahrokh Zand-Parsa, Sajad Jamshidi. Accuracy assessment of forest mapping in MODIS land cover dataset using fuzzy set theory. Forest Resources Resilience and Conflicts. 2021; ():165-183.
Chicago/Turabian StyleMaryam Naghdizadegan Jahromi; Mojtaba Naghdyzadegan Jahromi; Hamid Reza Pourghasemi; Shahrokh Zand-Parsa; Sajad Jamshidi. 2021. "Accuracy assessment of forest mapping in MODIS land cover dataset using fuzzy set theory." Forest Resources Resilience and Conflicts , no. : 165-183.
This paper examines regional cooperation in disaster risk management (DRM) in the transboundary regions of five Central Asian states: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Regional cooperation to reduce disaster potential is a rather recent endeavour both internationally and in the region. Cooperation to enhance environmental security in post-Soviet Central Asia is slowly strengthening monitoring, planning, and prevention of natural disasters with a new approach that anticipates risks and hazards and seeks to reduce the likelihood of disasters instead of responding to the aftermath. Empowerment of regional associations to coordinate states’ activities to understand and solve common problems is needed. The legacy of the Soviet past and the contemporary states’ efforts to participate in regional cooperative organizations are reviewed and the prospects for new instruments for DRM cooperation are discussed. The needs are multifaceted and complex, but there are glimmers of promise for regional and borderland cooperation.
Nadira G. Mavlyanova; Viacheslav A. Lipatov; John P. Tiefenbacher. Regional Cooperative Disaster Risk Management in Central Asian Borderlands. Journal of Borderlands Studies 2021, 1 -23.
AMA StyleNadira G. Mavlyanova, Viacheslav A. Lipatov, John P. Tiefenbacher. Regional Cooperative Disaster Risk Management in Central Asian Borderlands. Journal of Borderlands Studies. 2021; ():1-23.
Chicago/Turabian StyleNadira G. Mavlyanova; Viacheslav A. Lipatov; John P. Tiefenbacher. 2021. "Regional Cooperative Disaster Risk Management in Central Asian Borderlands." Journal of Borderlands Studies , no. : 1-23.
Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988–2020), remote sensing images (e.g., MODIS, Landsat 5–8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967–0.999, MAE = 0.022–0.117, RMSE = 0.029–0.148, RAE = 4.48–23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929–0.967), corrected classified instances (CCI: 96.45–98.35), area under the curve (AUC: 0.983–0.997), and Gini coefficients (0.966–0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.
Mahfuzur Rahman; Ningsheng Chen; Ahmed Elbeltagi; Monirul Islam; Mehtab Alam; Hamid Reza Pourghasemi; Wang Tao; Jun Zhang; Tian Shufeng; Hamid Faiz; Muhammad Aslam Baig; Ashraf Dewan. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. Journal of Environmental Management 2021, 295, 113086 .
AMA StyleMahfuzur Rahman, Ningsheng Chen, Ahmed Elbeltagi, Monirul Islam, Mehtab Alam, Hamid Reza Pourghasemi, Wang Tao, Jun Zhang, Tian Shufeng, Hamid Faiz, Muhammad Aslam Baig, Ashraf Dewan. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. Journal of Environmental Management. 2021; 295 ():113086.
Chicago/Turabian StyleMahfuzur Rahman; Ningsheng Chen; Ahmed Elbeltagi; Monirul Islam; Mehtab Alam; Hamid Reza Pourghasemi; Wang Tao; Jun Zhang; Tian Shufeng; Hamid Faiz; Muhammad Aslam Baig; Ashraf Dewan. 2021. "Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh." Journal of Environmental Management 295, no. : 113086.
When it comes to projecting the potential effects of climate change on hydro-climatic variables using time-series models, the conventional approach has been to examine correlations with exogenous variables. Establishing correlations among endogenous and exogenous variables, however, cannot guarantee that there is a cause-effect relationship among the variables. This study, therefore, used Granger-causality for a more accurate alternative to the exogenous variables needed to expand time-series models. To demonstrate this, Maharlou Lake, Iran was selected for a case study not only because this inland water body has been exhibiting unprecedented depletion patterns recently, but also because studies are projecting that a changing local climate could add pressure to the region’s water resources. Both restricted and extended models reveal that shrinkage observed in the lake’s time-series data is expected to continue in the near future. This depletion, however, is projected to be more pronounced in August, September, and October, and milder in February, March, and April. Furthermore, the results from the extended model hint at a more severe pattern of shrinkage rooted in the adverse impacts of projected climate change.
Babak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies. Hydrological Sciences Journal 2021, 1 .
AMA StyleBabak Zolghadr-Asli, Maedeh Enayati, Hamid Reza Pourghasemi, Mojtaba Naghdyzadegan Jahromi, John P. Tiefenbacher. Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies. Hydrological Sciences Journal. 2021; ():1.
Chicago/Turabian StyleBabak Zolghadr-Asli; Maedeh Enayati; Hamid Reza Pourghasemi; Mojtaba Naghdyzadegan Jahromi; John P. Tiefenbacher. 2021. "Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies." Hydrological Sciences Journal , no. : 1.
This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC = 0.967, TSS = 0.91), followed by RF (AUC = 0.936, TSS = 0.87), EBF (AUC = 0.907, TSS = 0.83), and IoE (AUC= 0.88, TSS = 0.8).
Elham Rafiei Sardooi; Hamid Reza Pourghasemi; Ali Azareh; Farshad Soleimani Sardoo; John J. Clague. Comparison of statistical and machine learning approaches in land subsidence modelling. Geocarto International 2021, 1 -21.
AMA StyleElham Rafiei Sardooi, Hamid Reza Pourghasemi, Ali Azareh, Farshad Soleimani Sardoo, John J. Clague. Comparison of statistical and machine learning approaches in land subsidence modelling. Geocarto International. 2021; ():1-21.
Chicago/Turabian StyleElham Rafiei Sardooi; Hamid Reza Pourghasemi; Ali Azareh; Farshad Soleimani Sardoo; John J. Clague. 2021. "Comparison of statistical and machine learning approaches in land subsidence modelling." Geocarto International , no. : 1-21.
Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics.
A’Kif Al-Fugara; Ali Mabdeh; Mohammad Ahmadlou; Hamid Pourghasemi; Rida Al-Adamat; Biswajeet Pradhan; Abdel Al-Shabeeb. Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing. ISPRS International Journal of Geo-Information 2021, 10, 382 .
AMA StyleA’Kif Al-Fugara, Ali Mabdeh, Mohammad Ahmadlou, Hamid Pourghasemi, Rida Al-Adamat, Biswajeet Pradhan, Abdel Al-Shabeeb. Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing. ISPRS International Journal of Geo-Information. 2021; 10 (6):382.
Chicago/Turabian StyleA’Kif Al-Fugara; Ali Mabdeh; Mohammad Ahmadlou; Hamid Pourghasemi; Rida Al-Adamat; Biswajeet Pradhan; Abdel Al-Shabeeb. 2021. "Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing." ISPRS International Journal of Geo-Information 10, no. 6: 382.
This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.
Xin Yang; Rui Liu; Mei Yang; Jingjue Chen; Tianqiang Liu; Yuantao Yang; Wei Chen; Yuting Wang. Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. Remote Sensing 2021, 13, 2166 .
AMA StyleXin Yang, Rui Liu, Mei Yang, Jingjue Chen, Tianqiang Liu, Yuantao Yang, Wei Chen, Yuting Wang. Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping. Remote Sensing. 2021; 13 (11):2166.
Chicago/Turabian StyleXin Yang; Rui Liu; Mei Yang; Jingjue Chen; Tianqiang Liu; Yuantao Yang; Wei Chen; Yuting Wang. 2021. "Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping." Remote Sensing 13, no. 11: 2166.
Land subsidence (LS) is significant problem that can lead to casualties, destruction of infrastructure, and socio-economic and environmental problems. In this study, we examine the Damghan Plain of Iran where LS poses a major obstacle to growth and management of the region. Dagging and random subspace (RSS) as meta- or ensemble-classifiers of a radial basis function neural network (RBFnn) were combined into two novel-ensemble intelligence approaches (Dagging-RBFnn and RSS-RBFnn) to predict and map the susceptibility of land units to subsidence. The goodness-of-fit (of training data) and prediction accuracy (of testing data) for the ensemble models were contrasted with the RBFnn, which is used as the benchmark for improvement. Details of 120 LS locations were examined and the data for twelve LS conditioning factors (LSCFs) were compiled. The LS points were randomly divided into four groups or folds, each comprised of 25 percent of the cases. The novel ensemble models were constructed using 75 percent (3 folds) and tested with the remaining 25 percent (onefold) in a four-fold cross-validation (CV) mechanism. Information-gain ratio and multicollinearity tests were used to select the LSCFs that would be used to estimate LS probabilities. The importance of each factor was calculated using a random forest (RF) model. The most important LSCFs were groundwater drawdown, land uses and land covers, elevation, and lithology. Twelve land subsidence susceptibility maps were generated using the k-fold CV approaches as each of the three models (RBFnn, Dagging-RBFnn and RSS-RBFnn) was applied to each of the four folds. The LS susceptibility models reveal a strong probability for LS on 15% to 24% of the plain. All of the maps generated achieved adequate levels of prediction accuracies and goodness-of-fits. The Dagging-RBFnn ensemble yielded the most robust maps, however. The ensemble of Dagging-RBFnn enhances the accuracy of modeling but the opposite condition was found for the RSS-RBFnn ensemble. It is evident that ensembles with meta classifiers might not always increase the accuracy of the base classifier. Overall, the southern part of the plain shows the highest LS risk. The results of this study suggests that groundwater withdrawal levels should be tracked and possibly restricted in regions with higher (extreme or moderate) probabilities of LS. This demonstrates that new approaches can support land use planning and decision making to minimize LS and improve sustainability.
Alireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment 2021, 1 -23.
AMA StyleAlireza Arabameri, M. Santosh, Fatemeh Rezaie, Sunil Saha, Romulus Coastache, Jagabandhu Roy, Kaustuv Mukherjee, John Tiefenbacher, Hossein Moayedi. Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling. Stochastic Environmental Research and Risk Assessment. 2021; ():1-23.
Chicago/Turabian StyleAlireza Arabameri; M. Santosh; Fatemeh Rezaie; Sunil Saha; Romulus Coastache; Jagabandhu Roy; Kaustuv Mukherjee; John Tiefenbacher; Hossein Moayedi. 2021. "Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling." Stochastic Environmental Research and Risk Assessment , no. : 1-23.
This study performs flood hazard mapping and evaluates community flood coping strategies. In addition, it proposes a humanitarian aid information system (HAIS) to enhance emergency support for flood victims. First, a flood hazard map was prepared using the hydrodynamic model (HM)–FLO 2D coupled with a machine learning algorithm (MLA)-scaled conjugate gradient neural network (SCG-NN). The performance of the MLA was evaluated using a validation dataset and statistical measures such as the mean square error (MSE: 0.080), root mean square error (RMSE: 0.282), and coefficient of determination (R2 = 0.830). According to the generated flood hazard map, most of the study area was classified as low (47.85%) or moderate (27.47%) hazardous zones, whereas only a small portion was delineated as high (20.64%) or very high (4.04%) hazardous zones. The accuracy of the hazard map (HM-MLA) versus the ground truth was tested statistically and was found to be high. Second, an investigation of local flood management strategies revealed that the current information system is not well prepared for emergencies, including the quantification of emergency relief necessities. Therefore, an HAIS, which specifies hazard information and quantifies emergency aids (food items) for flood victims, can be an effective emergency preparedness tool. We calculated the required emergency aid considering satellite-derived flood data. Finally, we conclude that the proposed HAIS will help humanitarian organizations and government agencies coordinate and perform relief operations effectively in the worst-hit regions across the country.
Mahfuzur Rahman; Ningsheng Chen; Monirul Islam; Golam Iftekhar Mahmud; Hamid Reza Pourghasemi; Mehtab Alam; Abdur Rahim; Muhammad Aslam Baig; Arnob Bhattacharjee; Ashraf Dewan. Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm. Journal of Cleaner Production 2021, 311, 127594 .
AMA StyleMahfuzur Rahman, Ningsheng Chen, Monirul Islam, Golam Iftekhar Mahmud, Hamid Reza Pourghasemi, Mehtab Alam, Abdur Rahim, Muhammad Aslam Baig, Arnob Bhattacharjee, Ashraf Dewan. Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm. Journal of Cleaner Production. 2021; 311 ():127594.
Chicago/Turabian StyleMahfuzur Rahman; Ningsheng Chen; Monirul Islam; Golam Iftekhar Mahmud; Hamid Reza Pourghasemi; Mehtab Alam; Abdur Rahim; Muhammad Aslam Baig; Arnob Bhattacharjee; Ashraf Dewan. 2021. "Development of flood hazard map and emergency relief operation system using hydrodynamic modeling and machine learning algorithm." Journal of Cleaner Production 311, no. : 127594.
Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to the high demand for fresh groundwater. Therefore, the identification of potential groundwater zones is an important tool for groundwater occurrence, protection, and management purposes. More specifically, in arid and semi-arid regions, groundwater is one of the most important natural resources as it supplies water during the drought period. The present research study focused on the delineation of potential groundwater zones in Saveh City, the northern part of the Markazi Province in Iran. The groundwater potential mapping was prepared using hybrid deep learning and machine learning algorithm of the boosted tree (BT), artificial neural network (ANN), deep learning neural network (DLNN), deep learning tree (DLT), and deep boosting (DB). This study was carried out by using fourteen groundwater potential conditioning factors and 349 each for springs and non-springs points. The performance of each model was validated through statistical analysis of sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and receiver operating characteristic (ROC)-area under curve (AUC) analysis. The validation result showed that the success rate of AUC is very good for the DB model (0.87–0.99) and other models are also good i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), and DLT (0.83–0.91). Among the several factors used in this study altitude, rainfall, distance to fault and soil types are the more important conditioning factors for groundwater potential modeling. Finally, all the models in this study had high efficiency in groundwater potential mapping, but it is recommended to use the Deep Boost model due to the better results in future studies. The result of this work will be useful to planners for optimal use and future planning of groundwater.
Yunzhi Chen; Wei Chen; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Behzad Adeli; Saeid Janizadeh; Adrienn A. Dineva; Xiaojing Wang; Amirhosein Mosavi. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International 2021, 1 -21.
AMA StyleYunzhi Chen, Wei Chen, Subodh Chandra Pal, Asish Saha, Indrajit Chowdhuri, Behzad Adeli, Saeid Janizadeh, Adrienn A. Dineva, Xiaojing Wang, Amirhosein Mosavi. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International. 2021; ():1-21.
Chicago/Turabian StyleYunzhi Chen; Wei Chen; Subodh Chandra Pal; Asish Saha; Indrajit Chowdhuri; Behzad Adeli; Saeid Janizadeh; Adrienn A. Dineva; Xiaojing Wang; Amirhosein Mosavi. 2021. "Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential." Geocarto International , no. : 1-21.
The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.
Hilal Ahmad; Chen Ningsheng; Mahfuzur Rahman; Monirul Islam; Hamid Pourghasemi; Syed Hussain; Jules Habumugisha; Enlong Liu; Han Zheng; Huayong Ni; Ashraf Dewan. Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models. ISPRS International Journal of Geo-Information 2021, 10, 315 .
AMA StyleHilal Ahmad, Chen Ningsheng, Mahfuzur Rahman, Monirul Islam, Hamid Pourghasemi, Syed Hussain, Jules Habumugisha, Enlong Liu, Han Zheng, Huayong Ni, Ashraf Dewan. Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models. ISPRS International Journal of Geo-Information. 2021; 10 (5):315.
Chicago/Turabian StyleHilal Ahmad; Chen Ningsheng; Mahfuzur Rahman; Monirul Islam; Hamid Pourghasemi; Syed Hussain; Jules Habumugisha; Enlong Liu; Han Zheng; Huayong Ni; Ashraf Dewan. 2021. "Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models." ISPRS International Journal of Geo-Information 10, no. 5: 315.