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

Barman, Jonmenjoy; Ali, Syed Sadath; Biswas, Brototi; Das, Jayanta

doi:10.1016/j.nhres.2023.06.006

Cited by 18 publications

(3 citation statements)

References 79 publications

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“…The study of landslide prediction is crucial for effective land management, planning, and development in hilly and mountainous areas (Biswas et al, 2021;Barman et al, 2023;Jari et al, 2023). Developing an accurate landslide prediction model is a challenging task.…”

Section: Discussionmentioning

confidence: 99%

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Halder,

Srivast,

Ghosh

et al. 2024

Preprint

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Landslides pose significant threats to local ecological environments, causing loss of life and economic damage. This research focuses on enhancing landslide susceptibility prediction in West Bengal's Sub-Himalayan region using an innovative ensemble Recursive Feature Elimination (RFE) and meta-learning framework. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ExtraTrees), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Meta Classifier (MC) - were utilized alongside Remote Sensing and Geographic Information System techniques. Through feature selection, the ensemble identifies the most significant landslide conditioning factors. Evaluation metrics, including accuracy and AUC of the ROC curve, demonstrate the ensemble's superior predictive ability. Based on the findings, models perform well with LR (AUC = 0.935), SVM (AUC = 0.972), RF (AUC = 0.983), ExtraTrees (AUC = 0.985), GB (AUC = 0.987), and XGBoost (AUC = 0.987). However, the MC performed better than individual models with AUC = 0.987. The study's implications for land-use planning and disaster management in the region are discussed and by establishing a new benchmark for landslide susceptibility mapping, this research offers a promising approach for addressing similar environmental challenges worldwide, facilitating informed decision-making and mitigation efforts in geologically sensitive areas.

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Section: Discussionmentioning

confidence: 99%

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Halder,

Srivast,

Ghosh

et al. 2024

Preprint

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“…located in the southern part of the northern hemisphere, maximum rainfall was done by the influence of the southwest monsoon; thus, the maximum weights were given to south, southwest and flat slope direction (Barman et al 2023a). In the case of TWI, the maximum and minimum weights were assigned to 2.97-5.25 and 12.04-19.12, respectively, as the higher TWI value indicated more water accumulation (Razandi et al 2015).…”

Section: Relationship Between Influencing Factors and Groundwater Pot...mentioning

confidence: 99%

Detection of groundwater conditioning factors in a hilly environment

Barman,

VLH Zuali,

Bindajam

et al. 2024

Appl Water Sci

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Champhai, the rice bowl of Mizoram, is known for wet rice practices. Rapid urbanization and global climate change increased the demand for groundwater. Champhai city, being a hilly township in northeast India, is very difficult to identify the potential groundwater water availability. The present study aimed at groundwater potentiality zonation in Champhai town. For these purposes ten (10) factors from topographic, climatic, hydrological and other conditions were assigned weightage based on the AHP (analytical hierarchy process) technique. The aspect was given maximum weightage as the aspect working as the controlling factors of precipitation intensity, vegetation and soil type, while LULC (land use land cover) was assigned minimum weightage that unscientific human activities reduced infiltration rate. Subsequently, an addition of ten weighted rasters was performed for groundwater potentiality zonation. As a result, it was seen that the maximum part of the study area covered low potentiality zones (31.88%), whereas high and very high potential zone covered 14.73% and 5.08, respectively. A scientific validation technique, namely AUC (area under curve) value of 0.737, denoted a good classified groundwater water potentiality zone. Further, the single-layer sensitivity analysis highlighted that elevation, slope, aspect, infiltration number, distance to streams and rainfall had the maximum effective factors based on their effective weightage. The map removal sensitivity analysis highlighted that TWI (Topographic Wetness Index) and aspect had the maximum influence on the model. However, in a hilly environment like Champhai, the output of the current study will work as a blueprint for planning and decision making.

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“…The slope's aspect or exposure indicates the direction in which it is oriented (Barman et al, 2023). Conditioning exposure to sunlight and drying winds that control soil moisture and evapotranspiration (Tang et al, 2020) is an indicative factor of landslide susceptibility.…”

Section: Slope Aspectmentioning

confidence: 99%

Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach

Hamidi,

Bouramtane,

Abraham

et al. 2023

Ecol. Eng. Environ. Technol.

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Landslides are considered to be one of the most significant and critical natural hazards in the heterogeneous geomorphological setting of the Rif region of Morocco. Despite the high susceptibility to landslides, the region lacks detailed studies. Therefore, this research introduces four advanced machine learning methods, namely Support Vector Machine (SVM), Classification and Regression Trees (CART), Multivariate Discriminant Analysis (MDA), and Logistic Regression (LR), to perform landslide susceptibility mapping, as well as study of the connection between landslide occurrence and the complex regional geo-environmental context of Taounate province. Fifteen causative factors were extracted, and 255 landslide events were identified through fieldwork and satellite imagery analysis. All models performed very well (AUC > 0.954), while the CART model performed the best (AUC= 0.971). However, SVM demonstrated superior performance compared to other methods, achieving the highest accuracy (89.92%) and F1-measure (81.66%) scores on the training data, and the highest accuracy (83.01%), precision (81.74%), and specificity (79.46%) scores on the test data. The results do not necessarily indicate that LR and MDA have the lowest predictive ability, as they demonstrated high accuracy in terms of AUC and in some classification tasks. Moreover, they provide the significant advantage of easy interpretation of the geo-environmental processes that control landslides. Rainfall is the primary triggering factor of landslides in the study area. The majority of landslides occurred on slopes, particularly those located along rivers and faults, suggesting that landslides in the region are closely associated with active tectonics and precipitation. All four models predicted similar spatial distribution patterns in landslide susceptibility. The results showed that almost half of the area mainly in the north and northwest, has a very high susceptibility to landslides. The findings provide valuable references for land use management and the implementation of effective measures for landslide prevention.

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

Cited by 18 publications

References 79 publications

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Detection of groundwater conditioning factors in a hilly environment

Landslide Hazard Assessment in the Heterogeneous Geomorphological and Environmental Context of the Rif Region, Morocco – A Machine Learning Approach

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