Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes

Abstract

Landslides are most catastrophic and frequently occurred across the world. In mountainous areas of the globe, recurrent occurrences of landslide have caused huge amount of economic losses and a large number of casualties. In this research, we attempted to estimate the potential impact of climate and LULC on future landslide susceptibility in of Markazi Province of Iran. We considered the boosted tree (BT), random forest (RF) and extremely randomized tree (ERT) models for landslide susceptibility assessment in Markazi Province. The results of evaluation criteria showed that ERT model is most optimal than other models used in this study with AUC values of 0.99 and 0.93 for the training and validation datasets, respectively. According to the ERT model, the spatial coverage of the very high and high land slide susceptible zones for the current period, 2050s considering RCP 2.6 and 2050s considering RCP 8.5 are 428.5 km², 439.6 km² and 465.2 km², respectively. From this analysis it is clear that the impact of climate and LULC changes on future landslide susceptibility is prominent. The results of the present study help managers to reduce landslide damages, not only for current but also for future conditions, based on climate and LULC changes.

Keywords:

• Landslide
• climate change
• land-use change
• extremely randomized tree
• machine learning

Disclosure statement

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. There are no conflicts of interest to declare.

Availability of data and materials

The Data That Support The Findings Of This Study Are Available From The Corresponding Author, Upon Reasonable Request.

Acknowledgements

The Authors extend their thanks to the Deanship of Scientific Research at King Khalid University for funding this work through the large research groups under grant number RGP. 2/173/42.

Additional information

Funding

This research work was supported by the Deanship of Scientific Research at King Khalid University under Grant number RGP. 2/173/42.