Application of TOPSIS method for flood susceptibility mapping using Excel and GIS DOI Creative Commons
Rajib Mitra, Jayanta Das, Md. Kamruzzaman

и другие.

MethodsX, Год журнала: 2023, Номер 11, С. 102263 - 102263

Опубликована: Июнь 15, 2023

This study elaborately manifests a simplified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multicriteria decision-making (MCDM) approach that goals determine the disparity among distances between positive and negative ideal solutions. MCDM methods evaluate options based on variety of criteria using mathematical analytical methodologies. promotes more transparent objective process removing human biases subjective judgements. By considering comparative proximity optimal situation, TOPSIS considers negative-ideal alternatives. has concentrated normalization process, appropriate determination anti-ideal solution, metric utilized compute euclidean from best worst.•This expresses methodology as stated Hwang Yoon (1981). The categorization weight assignments have been executed expert's opinion existing literatures.•Integration technique with GIS properly performed production flood susceptibility map highly vulnerable region visual interpretation algorithm.•This kind investigation saved time sufficiently skilled specialized personnel in this field.

Язык: Английский

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India DOI Creative Commons
Jayanta Das, Pritam Saha, Rajib Mitra

и другие.

Heliyon, Год журнала: 2023, Номер 9(5), С. e16186 - e16186

Опубликована: Май 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.

Язык: Английский

Процитировано

50
Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm DOI
Jatan Debnath,

Jimmi Debbarma,

Amal Debnath

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(2)

Опубликована: Янв. 4, 2024

Язык: Английский

Процитировано

20
GIS-based flood risk assessment using multi-criteria decision analysis of Shebelle River Basin in southern Somalia DOI Creative Commons
Shuayb Abdinour Osman, Jayanta Das

SN Applied Sciences, Год журнала: 2023, Номер 5(5)

Опубликована: Апрель 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.

Язык: Английский

Процитировано

41
Flood susceptibility mapping using hybrid models optimized with Artificial Bee Colony DOI
Konstantinos Plataridis, Zisis Mallios

Journal of Hydrology, Год журнала: 2023, Номер 624, С. 129961 - 129961

Опубликована: Июль 19, 2023

Язык: Английский

Процитировано

31
Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method DOI
Yiqing Chen, Deyun Wang,

L. Zhang

и другие.

Environmental Science and Pollution Research, Год журнала: 2023, Номер 30(42), С. 96001 - 96018

Опубликована: Авг. 10, 2023

Язык: Английский

Процитировано

30
Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India DOI Creative Commons
Jonmenjoy Barman, Syed Sadath Ali, Brototi Biswas

и другие.

Natural Hazards Research, Год журнала: 2023, Номер 3(3), С. 508 - 521

Опубликована: Июнь 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.

Язык: Английский

Процитировано

28
Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques DOI Creative Commons
Jatan Debnath,

Dhrubojyoti Sahariah,

Nityaranjan Nath

и другие.

Modeling Earth Systems and Environment, Год журнала: 2023, Номер 10(2), С. 2393 - 2419

Опубликована: Дек. 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.

Язык: Английский

Процитировано

27
A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Floodplain, Assam DOI
Pranab Dutta, Sujit Deka

Journal of Cleaner Production, Год журнала: 2024, Номер 467, С. 142985 - 142985

Опубликована: Июнь 28, 2024

Язык: Английский

Процитировано

17
Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS DOI Creative Commons
Muhammad Ahsan Mukhtar, Donghui Shangguan, Yongjian Ding

и другие.

Frontiers in Environmental Science, Год журнала: 2024, Номер 12

Опубликована: Фев. 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.

Язык: Английский

Процитировано

16
Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch DOI Creative Commons
Mohamed Zhran, Karam Farrag, Aqil Tariq

и другие.

Environmental Sciences Europe, Год журнала: 2024, Номер 36(1)

Опубликована: Окт. 15, 2024

Abstract Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations method limitations. In this study, zonation in Nile districts of Damietta branch, Egypt, delineated assessed by integrating remote sensing with a geographic information system, an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness modified water positioning stream power Fournier drainage density, distance river, sediment transport lithology) used for producing susceptibility (FSZ) six parameters (total population, hospital, land use/land cover, population road road) utilized vulnerability zonation. Multicollinearity analysis applied identify highly correlated independent variables. Sensitivity studies have been assess effectiveness AHP model. The results indicate that high very classes cover 21.40% 8.26% area, respectively. 14.07%, 27.01%, 29.26% research respectively, zones classified as low, moderate found. Finally, FSZ validated using receiver operating characteristics curve area under (AUC) analysis. A higher AUC value (0.741) validation findings demonstrated validity approach. study will help planners, hydrologists, managers resources manage areas susceptible flooding reduce potential harm.

Язык: Английский

Процитировано

15