Browsing by Author "Deepali Gupta"

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Integrating FR, MFR and IV Models for Landslide Susceptibility Zonation Mapping in Joshimath Watershed, Uttarakhand, India
(Springer Science and Business Media Deutschland GmbH, 2025) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vishnu Himanshu Ratnam Pandey; Md Sarfaraz Asgher; Ashish Bhardwaj; Deepali Gupta
Joshimath watershed in Uttarakhand, India, is known for experiencing creep, subsidence, and frequent minor and major landslides. Identifying zones prone to landslides through landslide susceptibility zonation (LSZ) is crucial for city planners to mitigate risks and reduce potential losses. This study employs three widely used and accurate statistical models: Frequency Ratio (FR), Modified Frequency Ratio (MFR), and Information Value (IV) to assess LSZ. A dataset of 271 landslides, derived from time-series satellite images, was utilized, with 70% (190 events) allocated for model training and 30% for validation. The analysis considered fifteen factors influencing landslide susceptibility, including slope, aspect, curvature, proximity to drainage, proximity to faults, proximity to roads, geomorphon, landform, altitude, lithology, and LULC data from both Google Earth and ESRI, RR, SPI, and TWI were evaluated, offering a comprehensive view of the various factors that may affect landslide occurrence. Based on ranking, the most influential factors are geomorphon, proximity to faults and drainage, proximity to roads, and aspect. In contrast, LULC (ESRI), RR, altitude, lithology, and slope demonstrate limited influence, while TWI, SPI, and curvature are the least influential factors. The susceptibility maps were classified into three categories. The FR model identified 59.5% of the area as low susceptibility, 32.1% as medium, and 8.3% as high, with 65.9% of landslides occurring in high-susceptibility zones. The MFR model classified 48.3% of the area as low susceptibility, 27.1% as medium, and 24.6% as high, with 78.4% of landslides located in high-susceptibility zones. The IV model classified 37.8% of the area as low susceptibility, 41.1% as medium, and 21.2% as high, with 77.4% of landslides occurring in high-susceptibility zones. ROC analysis validated the models’ predictive capabilities, with the FR model achieving the highest accuracy in both the Landslide Susceptibility Index (LSI) and LSZ at 83.1% and 84.5% AUC, respectively. The MFR and IV models also demonstrated commendable performance, providing valuable insights for landslide risk assessment. The findings emphasize the importance of model selection in LSZ mapping, highlighting the FR and MFR models as effective tools for risk management and land-use planning in landslide-prone areas. This study contributes to landslide susceptibility modeling and provides a framework for future research in geological hazard assessment. © The Author(s), under exclusive licence to Shiraz University 2024.
Integrating FR, MFR and IV Models for Landslide Susceptibility Zonation Mapping in Joshimath Watershed, Uttarakhand, India
(Springer Science and Business Media Deutschland GmbH, 2024) Imran Khan; Ashutosh Kainthola; Harish Bahuguna; Vishnu Himanshu Ratnam Pandey; Md. Sarfaraz Asgher; Ashish Bhardwaj; Deepali Gupta
Joshimath watershed in Uttarakhand, India, is known for experiencing creep, subsidence, and frequent minor and major landslides. Identifying zones prone to landslides through landslide susceptibility zonation (LSZ) is crucial for city planners to mitigate risks and reduce potential losses. This study employs three widely used and accurate statistical models: Frequency Ratio (FR), Modified Frequency Ratio (MFR), and Information Value (IV) to assess LSZ. A dataset of 271 landslides, derived from time-series satellite images, was utilized, with 70% (190 events) allocated for model training and 30% for validation. The analysis considered fifteen factors influencing landslide susceptibility, including slope, aspect, curvature, proximity to drainage, proximity to faults, proximity to roads, geomorphon, landform, altitude, lithology, and LULC data from both Google Earth and ESRI, RR, SPI, and TWI were evaluated, offering a comprehensive view of the various factors that may affect landslide occurrence. Based on ranking, the most influential factors are geomorphon, proximity to faults and drainage, proximity to roads, and aspect. In contrast, LULC (ESRI), RR, altitude, lithology, and slope demonstrate limited influence, while TWI, SPI, and curvature are the least influential factors. The susceptibility maps were classified into three categories. The FR model identified 59.5% of the area as low susceptibility, 32.1% as medium, and 8.3% as high, with 65.9% of landslides occurring in high-susceptibility zones. The MFR model classified 48.3% of the area as low susceptibility, 27.1% as medium, and 24.6% as high, with 78.4% of landslides located in high-susceptibility zones. The IV model classified 37.8% of the area as low susceptibility, 41.1% as medium, and 21.2% as high, with 77.4% of landslides occurring in high-susceptibility zones. ROC analysis validated the models’ predictive capabilities, with the FR model achieving the highest accuracy in both the Landslide Susceptibility Index (LSI) and LSZ at 83.1% and 84.5% AUC, respectively. The MFR and IV models also demonstrated commendable performance, providing valuable insights for landslide risk assessment. The findings emphasize the importance of model selection in LSZ mapping, highlighting the FR and MFR models as effective tools for risk management and land-use planning in landslide-prone areas. This study contributes to landslide susceptibility modeling and provides a framework for future research in geological hazard assessment. © The Author(s), under exclusive licence to Shiraz University 2024.