Abstract:
Worldwide, landslides are the most frequently occurring
disaster that is very destructive and unpredictable in nature. A total
of 850 landslide events were detected during 2005–2020 in the Tehri
region of the Indian Himalayas. Many researchers have conducted
landslide susceptibility mapping (LSM) studies for this region using
different static landslide-causing factors. However, studies considering dynamic factors in predicting future landslide susceptibility
scenarios are inadequate. Hence in this study, both dynamic and
static factors were utilized in predicting future landslide susceptibility maps for the year 2050. The paper’s main objective is the
future prediction of LSM, considering future projections of land use
land cover (LULC) and climate variables (precipitation and temperature). To achieve this objective, first, the geospatial database
in three temporal categories, 2005–2010, 2010–2015, and 2015–2020,
was prepared for the historical landslide events. Second, the landslide-causing factors were optimized and utilized in LSM for 2010,
2015, and 2020. Third, projected LULC map was generated for the
year 2050 using the Artificial Neural Network-Cellular Automata
(ANN-CA) model. Fourth, CMIP6 climate projection maps were
prepared using the Indian Institute of Tropical Meteorology Earth
system model (IITM ESM) under four shared socioeconomic pathway (SSP) scenarios. Finally, the projected maps were used as the
driving parameter for the future prediction of LSM. The results
reveal a high increase in the built-up area (5%) and agriculture land
(4%) with a decrease in forest area (10%) in future LULC projections. The results of future LSM prediction under SSP 1–2.6, SSP
2–4.5, SSP 3–7.0, and SSP 5–8.5 climate scenarios show an increase
in very high landslide susceptibility class by 2%, 4%, 7%, and 9%
respectively. The predicted maps were validated utilizing the Kappa
coefficient verifies the reliability of the simulated future results.
