Article
Ecology
Purabi Sarkar, Pankaj Kumar, Dinesh Kumar Vishwakarma, Alaknanda Ashok, Ahmed Elbeltagi, Sandeep Gupta, Alban Kuriqi
Summary: Due to the complexity of human and ecological systems, soil erosion has become a significant issue. Therefore, the development of watershed approaches is crucial for understanding and forecasting future circumstances. This study explores the morphometric characteristics of the Pindar River watershed and uses various decision-making techniques to develop a suitable management plan. The findings demonstrate that morphometric criteria are highly effective in identifying erosion-prone locations, with the FAHP method showing superior accuracy and efficiency.
ECOLOGICAL INFORMATICS
(2022)
Article
Environmental Sciences
Daniel Assefa Negash, Mitiku Badasa Moisa, Biratu Bobo Merga, Firdissa Sedeta, Dessalegn Obsi Gemeda
Summary: This study estimated the annual soil loss in the study area through soil data and satellite images, revealing that soil erosion problems are mainly caused by mis-management of agricultural land and overexploitation of natural resources. The importance of soil erosion risk assessment was emphasized to influence policy makers in implementing effective mitigation measures.
ENVIRONMENTAL EARTH SCIENCES
(2021)
Article
Engineering, Environmental
Sarita Gajbhiye Meshram, Vijay P. Singh, Ercan Kahya, Mehdi Sepehri, Chandrashekhar Meshram, Mohd Abul Hasan, Saiful Islam, Pham Anh Duc
Summary: This study focused on watershed prioritization using morphometric parameters and fuzzy logic methods in GIS for Gusru Watershed in India. Evaluation of fourteen morphometric parameters showed that certain sub-watersheds were more susceptible to soil erosion.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Environmental Sciences
Soumita Sengupta, Sk Mohinuddin, Mohammad Arif
Summary: This study analyzed the characterization and prioritization of sub-watersheds in the Tenughat watershed based on morphometric parameters, dividing the entire watershed into five potential erosion zones and providing useful information for watershed managers to make informed decisions.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Muhammad Jamal Nasir, Waqas Ahmad, Changhyun Jun, Javed Iqbal, Sayed M. Bateni
Summary: This study conducted an erosion susceptibility assessment of 17 sub-watersheds in the Swat River watershed using a high-resolution digital elevation model and GIS-based analysis. The results showed that SW8, SW12, and SW15 were the most vulnerable to erosion, while SW1, SW2, and SW4 were the least vulnerable. The study also identified basin form and relief parameters as the key factors affecting soil erosion.
ENVIRONMENTAL EARTH SCIENCES
(2023)
Article
Environmental Sciences
Hamid Reza Pourghasemi, Fatemeh Honarmandnejad, Mahrooz Rezaei, Mohammad Hassan Tarazkar, Nitheshnirmal Sadhasivam
Summary: Water-induced erosion significantly impacts the sustainable development of land and water resources in the Qareaghaj catchment of Fars Province, Iran. Various MCDM models were compared to prioritize sub-watersheds and determine influencing factors of water erosion. Results show that WASPAS-AHP model had higher correlation with TOPSIS-AHP and VIKOR-AHP models, indicating its effectiveness in mapping erosion susceptibility and developing strategies for controlling water erosion in the study area.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Devendra Kumar, Arvind Dhaloiya, Ajeet Singh Nain, Mahendra Paal Sharma, Amandeep Singh
Summary: Soil erosion is a major concern at the watershed scale, with the study combining RUSLE modeling with remote sensing and GIS techniques to predict soil erosion in Nainital district, India. The results showed that the majority of the district is covered with forest, with annual average soil loss ranging from 20 to 80 t ha(-1) yr(-1) and prioritized watersheds for conservation efforts identified.
Article
Environmental Sciences
Olabanji Odunayo Aladejana, Ayomide Joshua Oraegbu, Babatunde Joseph Fagbohun
Summary: This study explores the potential of combining locally dominant soil erosion driving factors with the RUSLE model to assess soil erosion susceptibility within the OIO watershed. The results demonstrate the effectiveness and feasibility of this approach in evaluating soil erosion susceptibility within the watershed.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
C. J. Rakesh, Govindaraju, S. Lokanath, A. Kishor Kumar
Summary: The present study prioritizes 18 sub-watersheds in the Boranakanive reservoir catchment for soil erosion-threat analysis using Geospatial and weighted sum approach. Morphometric analysis was conducted using ArcGIS software and Aster DEM data. The results identified sub-watershed SW2 as most sustainable and SW17 as most affected by soil erosion. Recommendations for soil and water conservation practices were provided for highly prone sub-watersheds SW17, SW18, and SW13.
ENVIRONMENTAL EARTH SCIENCES
(2023)
Article
Water Resources
W. R. Singh, S. Barman, G. Tirkey
Summary: The morphologic parameters of a watershed are essential for prioritizing critical sub-watersheds for conservation practices and interventions. The use of GIS and remote sensing technologies has made the process of determining these parameters easier, cheaper, and faster. In a study of the Dudhnai river basin, seven sub-watersheds were ranked based on vulnerability to soil erosion, revealing areas in need of greater attention for conservation measures.
APPLIED WATER SCIENCE
(2021)
Article
Environmental Sciences
Muhammad Ramdhan Olii, Aleks Olii, Ririn Pakaya, Muhammad Yasin Umsini Putra Olii
Summary: Soil erosion is the main problem in land management faced by developing countries globally. This study conducted a mapping of soil erosion-prone areas in the Bone Watershed, Indonesia, using a Digital Elevation Model and geographic information system methods. The study considered ten topographic factors and found that slope, sediment transport index, and flow length to the nearest stream had the most significant impact on soil erosion. The research identified high and very high soil erosion-prone zones, accounting for 22.7% and 0.3% of the research area, respectively, and validated the mapping results with a fair classification accuracy of 75.4% using the AUC ROC approach.
ENVIRONMENTAL EARTH SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Melese Baye Hailu, S. K. Mishra, Sanjay K. Jain
Summary: Soil erosion is a global concern that has negative impacts on agriculture and dam reservoir storage capacity. Developing effective land management strategies in large watersheds is difficult due to high soil conservation expenditure. This study used the Soil and Water Assessment Tools model to identify vulnerable areas for erosion and found that six subbasins in the Tekeze watershed require urgent attention. The findings can guide land managers in reducing soil erosion and enhancing agricultural productivity.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Environmental Sciences
Natnael Agegnehu Ayele, Hasan Raja Naqvi, Daniel Alemayehu
Summary: This study assesses soil erosion in Ethiopia using RUSLE and SYI models and prioritizes sub-watersheds for conservation treatment. The results show a strong relationship between erosion rate and topography, with severe erosion observed at higher elevations. Immediate action is recommended for conservation treatment in these areas.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
S. G. Meshram, S. Tirivarombo, C. Meshram, E. Alvandi
Summary: In this research, fuzzy MCDM approaches were used to prioritize sub-watersheds in the Manot watershed in order to identify areas prone to soil erosion. The findings showed that morphometric parameters and the fuzzy MCDM approach have high efficiency in recognizing areas that are vulnerable to erosion.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
(2023)
Article
Environmental Studies
Wakjira Takala Dibaba, Tamene Adugna Demissie, Konrad Miegel
Summary: Excessive soil loss and sediment yield in the highlands of Ethiopia are major contributors to land degradation, affecting water resources and existing water infrastructure. The study in the Finchaa catchment revealed areas with high erosion risks and identified priority areas for soil conservation practices. Implementing effective management practices, such as contour farming and soil bund, can significantly reduce sediment yields and mitigate soil erosion in the region.
Article
Engineering, Environmental
Alireza Arabameri, M. Santosh, Fatemeh Rezaie, Sunil Saha, Romulus Costache, Jagabandhu Roy, Kaustuv Mukherjee, John Tiefenbacher, Hossein Moayedi
Summary: The study examined land subsidence in the Damghan Plain of Iran, using novel ensemble intelligence approaches to generate susceptibility maps based on twelve conditioning factors. The results showed the southern part of the plain has the highest risk of land subsidence, suggesting that groundwater withdrawal levels should be monitored and potentially restricted in regions with higher probabilities of land subsidence. The study highlights the importance of new approaches in supporting land use planning and decision making to reduce land subsidence and enhance sustainability.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Environmental Sciences
Sunil Saha, Amiya Gayen, Priyanka Gogoi, Barnali Kundu, Gopal Chandra Paul, Biswajeet Pradhan
Summary: This study aimed to produce a drought vulnerability map in Jharkhand, India using novel ensemble machine learning algorithms. The results showed that the PSO-RF algorithm had the best performance among all approaches evaluated. The produced vulnerability maps can be helpful for policy intervention to minimize drought vulnerability.
GEOCARTO INTERNATIONAL
(2022)
Correction
Engineering, Environmental
Sunil Saha, Amiya Gayen, Bijoy Bayen
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Engineering, Environmental
Sunil Saha, Amiya Gayen, Bijoy Bayen
Summary: The study used deep learning and benchmark machine learning methods to prepare flood susceptibility maps for the Kunur River Basin, with results indicating that the deep learning model had slightly better prediction capacity compared to benchmark machine learning models.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Engineering, Environmental
Sunil Saha, Anik Saha, Tusar Kanti Hembram, Kanu Mandal, Raju Sarkar, Dhruv Bhardwaj
Summary: This research aims to generate a landslide susceptibility map for a specific river basin in India using machine learning ensemble models. The study analyzes a landslide inventory and incorporates 17 landslide conditioning factors to prepare the map. The CNN model is found to be the most effective for predicting landslide susceptibility in the study area, and the methods used can be applied globally.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2022)
Article
Engineering, Environmental
Raju Das, Sunil Saha
Summary: This study used computational intelligence and machine learning methods to create groundwater potential maps for Malda district in India, identifying the key factors influencing groundwater potential and evaluating the accuracy of the maps. The results showed that the ANN-Bagging ensemble model achieved the highest accuracy and could be used as an important tool for identifying groundwater potential areas.
GROUNDWATER FOR SUSTAINABLE DEVELOPMENT
(2022)
Article
Environmental Sciences
R. S. Ajin, Sunil Saha, Anik Saha, Aparna Biju, Romulus Costache, Sekhar L. Kuriakose
Summary: This study utilizes various models and their ensemble models to identify landslide susceptibility zones in Idukki district. The results show that all models have good performance, and the identified zones will help in implementing effective mitigation measures.
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Sunil Saha, Anik Saha, Bishnu Roy, Raju Sarkar, Dhruv Bhardwaj, Barnali Kundu
Summary: In this research, various machine learning techniques and Particle Swarm Optimization method were used to predict landslide susceptibility in Kottiyam town of Kerala, India. The results showed that the PSO algorithm can effectively improve the accuracy and efficiency of the models, with the Random Tree-PSO model performing the best in predicting landslide susceptibility.
EARTH SCIENCE INFORMATICS
(2022)
Article
Environmental Sciences
Sunil Saha, Anik Saha, Tusar Kanti Hembram, Barnali Kundu, Raju Sarkar
Summary: This study uses DLNN and various SVM kernel functions to construct a landslide susceptibility assessment model. The results show that the DLNN, DLNN-SVM (RBF), and DLNN-SVM (Linear) models perform well.
GEOCARTO INTERNATIONAL
(2022)
Article
Meteorology & Atmospheric Sciences
Sunil Saha, Barnali Kundu, Anik Saha, Kaustuv Mukherjee, Biswajeet Pradhan
Summary: Drought is a natural and complex climatic hazard with consequences for both natural and socio-economic contexts. This study used deep learning algorithms to assess drought vulnerability and developed a drought vulnerability map (DVM) for the monsoon climate dominant region of West Bengal, India. The results show that nearly 24% of the study area is highly vulnerable to drought.
THEORETICAL AND APPLIED CLIMATOLOGY
(2023)
Article
Ecology
Aishwarya Sinha, Suresh Nikhil, Rajendran Shobha Ajin, Jean Homian Danumah, Sunil Saha, Romulus Costache, Ambujendran Rajaneesh, Kochappi Sathyan Sajinkumar, Kolangad Amrutha, Alfred Johny, Fahad Marzook, Pratheesh Chacko Mammen, Kamal Abdelrahman, Mohammed S. Fnais, Mohamed Abioui
Summary: This research uses geospatial tools, AHP, and fuzzy-AHP models to identify wildfire risk zones in Wayanad Wildlife Sanctuary and Kedarnath Wildlife Sanctuary. Both natural and anthropogenic factors contribute to the fire occurrences in these areas. The validation of the risk maps shows that both models have high prediction accuracy, with the F-AHP model performing slightly better. The created models can be used to implement effective policies to reduce the impact of fires in similar protected areas.
Article
Environmental Studies
Sheela Bhuvanendran Bhagya, Anita Saji Sumi, Sankaran Balaji, Jean Homian Danumah, Romulus Costache, Ambujendran Rajaneesh, Ajayakumar Gokul, Chandini Padmanabhapanicker Chandrasenan, Renata Pacheco Quevedo, Alfred Johny, Kochappi Sathyan Sajinkumar, Sunil Saha, Rajendran Shobha Ajin, Pratheesh Chacko Mammen, Kamal Abdelrahman, Mohammed S. Fnais, Mohamed Abioui
Summary: The study aims to assess the landslide susceptibility of high-range local self-governments (LSGs) in Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models, and compare the performance of existing landslide susceptible maps. The identification of landslide-susceptible areas and factors will help decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation to safer terrain.
Article
Environmental Sciences
Gopal Chandra Paul, Sunil Saha
Summary: The supply of water plays a crucial role in regional crop production and food security. The CropWRA model is introduced to assess the satisfaction level of crop water requirements and promote sustainable water management in agriculture. By considering various factors such as DEM, hydrological and climatic data, and crop properties, the model calculates indices related to crop combination, water availability, and accessibility in the Bansloi River basin. Advanced machine learning algorithms are used to calculate the crop water satisfied degree and requirement by incorporating variables like atmospheric conditions, Landsat indices, and energy balance components for soil moisture estimation. The average crop water demand is 1.92 m, with a range of 1.58 to 2.26 m. The CropWSD varies from 17% to 116% due to variations in topography, river system, crop combination, land use, and water utilization. The average crop water satisfied degree is 59%, with 71% of the total area falling between 40% and 60% CropWSD level. The CropWRA model can be applied for sustainable water resource management, irrigation infrastructure development, and utilization of modern technologies.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
K. Amrutha, Jean Homian Danumah, S. Nikhil, Sunil Saha, A. Rajaneesh, Pratheesh C. Mammen, R. S. Ajin, Sekhar L. Kuriakose
Summary: Wildfires pose a major threat to the protected areas in the Western Ghats. This study used the AHP method to map the fire risk zones and evaluate the effectiveness of forest management in fire prevention. The results showed that a significant number of fires occur in high-risk areas and that the forest management initiatives are effective in the core zone of the national park. The findings of this study will assist decision-makers in implementing fire prevention measures.
JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS
(2022)
Article
Environmental Sciences
Sunil Saha, Anik Saha, Manob Das, Anamika Saha, Raju Sarkar, Arijit Das
Summary: The study found that rapid urbanization in India's three major urban agglomerations in the East has led to changes in climate and health risks, with higher temperatures observed in high density residential zones. Analysis of remote sensing data can help understand spatial variations of urban heat islands, aiding in effective land use planning for small and medium size cities in India.
REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
(2021)
