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

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

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Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms

Earth Science Informatics, Год журнала: 2024, Номер 17(5), С. 4859 - 4873

Опубликована: Авг. 5, 2024

Язык: 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, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 25, 2025

Язык: Английский

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Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 10, 2025

Язык: Английский

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Comprehensive landslide prediction mapping using bivariate statistical models of Mizoram state of Northeast India

Journal of Spatial Science, Год журнала: 2024, Номер 69(3), С. 963 - 993

Опубликована: Апрель 15, 2024

Landslides in the state of Mizoram result damage to life and properties annually. The study focuses on landslide susceptibility zones by frequency ratio (FR), evidential belief function (EBF) index entropy (IOE) models. A total 1,486 points were used build a relationship between 16 factors occurrences. results reveal 14.44%, 19.64% 3.55% area as very high susceptible FR, EBF IOE models, respectively. AUC support adoption model land use planning decision-making processes enhance natural resource management mitigate risks Mizoram.

Язык: Английский

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Flash Flood Susceptibility Mapping of North-east Depression of Bangladesh using Different GIS based Bivariate Statistical Models

Watershed Ecology and the Environment, Год журнала: 2024, Номер 6, С. 26 - 40

Опубликована: Янв. 1, 2024

Flash flood causes severe damage to the environment and human life across world, no exception is Bangladesh. Severe flash floods affect northeastern portion of Bangladesh in early monsoon pose a serious threat every aspect socioeconomic development environmental sustainability. To manage reduce loss, map susceptible zones plays key role. Thus, aim this research flood-susceptible areas haor utilizing GIS-based bivariate statistical models. The models utilized are frequency ratio (FR), weights evidence (WoE), certainty factor (CF), Shanon's entropy (SE) information value (IV). Among 250 identified locations, 80% data was used for training purposes 20% testing purposes. Eleven selected conditioning factors include elevation, slope, aspect, curvature, TWI, TRI, SPI, distance stream, stream density, rainfall physiography. calculated assigned using ArcGIS prepare final maps. Results AUC ROC indicate WoE (success rate = 0.833 prediction =0.925) best model susceptibility mapping followed by FR 0.828 =0.928) SE 0.827 =0.923). According models, topographic (flat area) hydrologic significantly control occurrence study area. prepared maps will be helpful disaster managers master planners

Язык: Английский

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Geospatial Assessment and Mapping Landslide Susceptibility for the Garo Hills Division, Meghalaya, India

Geological Journal, Год журнала: 2025, Номер 60(5), С. 1184 - 1201

Опубликована: Фев. 19, 2025

ABSTRACT Creating accurate and effective Landslide Susceptibility (LS) maps can aid disaster prevention mitigation efforts provide sufficient public safety. The primary aim of this study is to develop an LS map for the Garo Hills region in Meghalaya, India, using weight evidence (WoE), frequency ratio (FR), Shannon entropy (SE) methods. A comprehensive landslide inventory catalogued 98 events from 2000 2023 analysis, nine key geographical environmental parameters were prepared. Conducted multicollinearity correlation analysis identify mitigate collinearity issues between factors. model's performance was analysed through area under curve (AUC) value receiver operating characteristic (ROC) curves three recent landslides. results showed that FR method achieved highest accuracy, with successive rate (SRC) AUC predictive (PRC) values 0.860 0.940, respectively, classified susceptibility at sites as high, moderate, low. WoE effectively identified landslides site high very zones, achieving SRC PRC 0.844 0.915, respectively. SE robust predicting landslide‐prone areas, comparable other methods (0.913), though its (0.771) lower. Developed revealed zones account approximately 10% 3% area, predominantly near roads, steep slopes, higher elevations. information valuable civilians government authorities involved hazard monitoring management.

Язык: Английский

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Advanced machine learning techniques for enhanced landslide susceptibility mapping: Integrating geotechnical parameters in the case of Southwestern Cyprus

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Апрель 3, 2025

Язык: Английский

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Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach

Environmental Science and Pollution Research, Год журнала: 2023, Номер 31(41), С. 53767 - 53784

Опубликована: Авг. 11, 2023

Язык: Английский

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Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador)

Heliyon, Год журнала: 2023, Номер 9(9), С. e20170 - e20170

Опубликована: Сен. 1, 2023

Landslides are one of the natural phenomena with more negative impacts on landscape, resources, and human health worldwide. Andean geomorphology, urbanization, poverty, inequality make it vulnerable to landslides. This research focuses understanding explanatory landslide factors promoting quantitative susceptibility mapping. Both tasks supply valuable knowledge for region, focusing territorial planning risk management support. work addresses following questions using province Azuay-Ecuador as a study area: (i) How do EFA LR assess significance occurrence factors? (ii) Which most significant analysis in an context? (iii) What is map area? The methodological framework uses techniques describe behavior. models based historical inventory 665 records. identified NDVI, NDWI, altitude, fault density, road PC2 factors. latter factor represents standard deviation, maximum value precipitation, rainfall wet season (January, February, March). model was built from 7 latent factors, which explained 55% accumulated variance, medium item complexity 1.5, RMSR 0.02, TLI 0.89. technique also TWI, distance, plane curvature, distance important LR's model, AIC 964.63, residual deviance 924.63, AUC 0.92, accuracy 0.84, Kappa 0.68, shows statistical slope, roads geology, land cover encompasses time-series including vegetation weather dynamism occurrence. Finally, this replaces traditional qualitative expert knowledge, approaches area region.

Язык: Английский

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