Modelling and mapping of landslide susceptibility regulating potential ecosystem service loss: an experimental research in Saudi Arabia

Abstract

The study aims to create a novel artificial intelligence model-based landslide susceptibility model (LSM) at Aqabat, Saudi Arabia. For LSM, a combination of bagging, dagging, random forest (RF) ensemble with locally weighted learning (LWL), viz. bagging-LWL, dagging-LWL, and RF-LWL has been developed. The 50 landslide areas were divided into two categories training (40) and testing (10). For training datasets, the LWL-Bagging model had the highest AUC value of ROC curve (AUC-0.91), followed by LWL-RF (AUC-0.881), LWL-Dagging (AUC-0.88) and LWL (AUC-0.875). For testing datasets, AUC values of ROC curve of 0.891, 0.876, 0.868, and 0.844 were found for LWL-Bagging, LWL-RF, LWL-Dagging, and LWL, respectively. Based on AUC value, all hybrid machines learning models performed well, but LWL-Bagging model performed as the best fit model. The potential ecosystem loss services were assessed by assessing possible landslide hazards. The research delivers reliable results to influence policy decisions on ecosystem management and landslide-assessment.

Keywords:

• Ecosystem services
• landslide susceptibility
• ensemble machine learning
• data mining
• return periods

Acknowledgments

Authors thankfully acknowledge the Deanship of Scientific Research for proving administrative and financial supports. Funding for this research was given under award numbers RGP2/185/43 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.