Projecting the effect of climate change on multiple Geomorphological hazard using machine learning data driven approaches

Land subsidence (LS) and collapsed pipes (CP) pose environmental and socio-economic threats in arid and semi-arid regions. This study assesses the effect of climate change to address these problems in Khorasan-Razavi province, Iran. Thus, we mapped soil landforms susceptible to LS and CP based on climatic, geolocic, topoghraphic, hydrologic and edaphic variables using an ensemble forecasting approach. Additionally, we predicted the future susceptibility of CP and LS based on two future emission scenario pathways (SSP 5-8.5 and SSP 1-2.6), in 2030, 2050, 2070, and 2090. The assessment showed that the area under the ROC curve (AUC) indicated that the ensemble model accurately predicted the distribution of CP and LS (AUC > 0.8). Slope and clay content proved to be the most important factors affecting CP, whereas distance from faults and precipitation seasonality played more roles in LS susceptibility. The classification results indicated varying susceptibility levels to CP and LS in Khorasan-Razavi province, with approximately 31.58% categorized as low and 15.24% as very high LS susceptibility, while 42.71% were in the low CP susceptibility class. Overall, 57.16% of the area is safe from both hazards; however, 6.16% is vulnerable to both hazards, with more than 35% at risk for at least one hazard. Future prediction models suggest that up to approximately 4% of the area will consist susceptible to both hazards under both scenario emissions and less than 1% of the study area will reduce susceptibility for both studied hazards in future. The majority of regions that remain susceptible are in the southern province. These results guide for soil management to protect soil and water from the effects of humans and climate alternation in poor areas worldwide.