GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Heliyon, Journal Year: 2023, Volume and Issue: 9(5), P. e16186 - e16186

Published: May 1, 2023

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These models were tested high landslides-prone humid sub-tropical type Upper Tista basin Darjeeling-Sikkim Himalaya by integrating GIS remote sensing. The inventory map consisting 477 locations was prepared, about 70% all data utilized training model, 30% used to validate it after training. A total fourteen triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance stream, road, NDVI, LULC, rainfall, modified fournier lithology) taken into consideration preparing LSMs. multicollinearity statistics revealed no collinearity problem among causative factors this study. Based on FR, MIV, IOE, SI, EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, 14.17% areas, respectively, identified very landslide-prone zones. also that IOE model has highest accuracy 95.80%, followed SI (92.60%), MIV (92.20%), FR (91.50%), (89.90%) models. Consistent with actual distribution landslides, high, medium hazardous zones stretch along River major roads. suggested have enough usage mitigation long-term land use planning study area. Decision-makers local planners may utilise study's findings. techniques determining can be employed other Himalayan regions manage evaluate hazards.

Language: Английский

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Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(2)

Published: Jan. 4, 2024

Language: Английский

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Assessment of agricultural land use suitability using TOPSIS and VIKOR models: a case study of Koch Bihar district, West Bengal

Arabian Journal of Geosciences, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 25, 2025

Language: Английский

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GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia

SN Applied Sciences, Journal Year: 2023, Volume and Issue: 5(5)

Published: April 11, 2023

Abstract Floods are the most common and expensive natural calamity, affecting every country. Flooding in Shebelle River Basin (SRB) southern Somalia has posed a significant challenge to sustainable development. The main goal of this study was analyze flood hazard, vulnerability risk part SRB using GIS-based Multi-Criteria Decision Analysis (MCDA). hazard map constructed seven important causative factors: elevation, slope, drainage density, distance river, rainfall, soil geology. results demonstrate that very low, moderate, high, high zones correspond 10.92%, 24.97%, 29.13%, 21.93% 13.04% area SRB, respectively. created five spatial layers: land use/land cover, population road, Global man-made impervious surface (GMIS), Human built-up settlement extent (HBASE). In addition, susceptibility maps were used create map. for Basin, 27.6%, 30.9%, 23.6%, 12.1%, 5.7% zones, Receiver Operating Characteristics-Area Under Curve (ROC-AUC) model exhibited good prediction accuracy 0.781. majority basin is at flooding moderate ranges; however, some tiny areas ranges. Flood should be provided distributed authorities responsible protection so people aware locations.

Language: Английский

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Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(42), P. 96001 - 96018

Published: Aug. 10, 2023

Language: Английский

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Flood susceptibility mapping using hybrid models optimized with Artificial Bee Colony

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129961 - 129961

Published: July 19, 2023

Language: Английский

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Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India

Natural Hazards Research, Journal Year: 2023, Volume and Issue: 3(3), P. 508 - 521

Published: June 25, 2023

The present study focuses on developing a landslide susceptibility zonation (LSZ) using GIS-based bivariate statistical model in the Lunglei district of Mizoram. Initially, 17 factors were selected after calculating multicollinearity test for LSZ. A inventory map was created based 234 historic events, which randomly divided into training (70%) and testing (30%) datasets. Using Index Entropy (IOE) model, nine causative identified as having significant weightage LSZ: elevation, slope, aspect, curvature, normalized difference vegetation index, geomorphology, distance to road, lineament, river. On other hand, such land use cover, stream power terrain ruggedness roughness, topographic wetness annual rainfall, position geology had negligible weightage. Based relative importance factors, two models developed: scenario 1, considered 2, all factors. results revealed that 16% 14% area very highly prone 1 respectively. high zone accounted 26% 25% To assess accuracy models, receiver operating characteristic (ROC) curve quality sum ratio method performed 30% data an equal number non-landslide points. under (AUC) 2 0.947 0.922, respectively, indicating higher efficiency 1. ratios 0.435 0.43 these results, LSZ mapping from is suitable policymakers address development risk reduction associated with landslides.

Language: Английский

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Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 10(2), P. 2393 - 2419

Published: Dec. 16, 2023

Abstract Climate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including Himalayan foothill region India. The Jia Bharali River basin, situated this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to environment local communities. Developing accurate reliable susceptibility models is crucial for effective prevention, management, adaptation strategies. In study, we aimed generate a comprehensive zone model catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, Frequency Ratio model, Fuzzy Logic (FL) Multi-criteria Decision Making based Analytical Hierarchy Process evaluate of basin. results revealed that approximately one-third basin area fell within moderate very high flood-prone zones. contrast, over 50% was classified as low demonstrated strong performance, ROC-AUC scores exceeding 70% MAE, MSE, RMSE below 30%. FL AHP were recommended application among areas similar physiographic characteristics due their exceptional performance training datasets. This study offers insights policymakers, regional administrative authorities, environmentalists, engineers working region. By providing robust research enhances prevention efforts thereby serving vital climate strategy regions. findings also implications disaster risk reduction sustainable development areas, contributing global towards achieving United Nations' Sustainable Development Goals.

Language: Английский

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Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Feb. 12, 2024

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges its sustainable development. This study aimed evaluate flood risk in region by employing GIS-based Multi-Criteria Decision Analysis (MCDA) approach big climate data records. By using comprehensive assessment model, hazard map was developed considering nine influential factors: rainfall, regional temperature variation, distance river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness (TWI), land use/land cover (LULC), curvature, soil type. The analytical hierarchy process (AHP) analysis assigned weights each factor integrated geospatial GIS generate maps, classifying levels into five categories. higher importance slope compared NDVI, TWI, LULC, weighted overlay obtained from reclassified maps of influencing factors identified 6% total area as very high, 36% 41% moderate, 16% low, 1% low risk. accuracy model demonstrated through Receiver Operating Characteristics-Area Under Curve (ROC-AUC) analysis, yielding commendable prediction 0.773. MCDA offers an efficient direct means modeling, utilizing fundamental data. serves valuable tool for decision-makers, enhancing awareness providing vital insights management authorities Valley. As future developments unfold, this remains indispensable resource preparedness Valley region.

Language: Английский

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A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Floodplain, Assam

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 467, P. 142985 - 142985

Published: June 28, 2024

Language: Английский

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