Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China

Chi Yang, Jinghan Wang, Shuyi Li, Ruihan Xiong, Xiaobo Li, Lin Gao, Xu Guo, Chuanming Ma, Hanxiang Xiong () and Yang Qiu ()
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Chi Yang: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Jinghan Wang: School of Energy Science and Engineering, Central South University, Changsha 410083, China
Shuyi Li: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Ruihan Xiong: State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430078, China
Xiaobo Li: Fifth Geological Brigade of Shandong Provincial Bureau of Geology and Mineral Exploration and Development, Tai’an 271000, China
Lin Gao: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Xu Guo: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Chuanming Ma: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Hanxiang Xiong: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Yang Qiu: School of Environmental Studies, China University of Geosciences, Wuhan 430074, China

Sustainability, 2024, vol. 16, issue 11, 1-26

Abstract: The land use change (LUC) and urbanization caused by human activities have markedly increased the occurrence of landslides, presenting significant challenges in accurately predicting landslide susceptibility despite decades of model advancements. This study, focusing on the Li River Valley (LRV) within the Yongding District, China, employs two common models, namely an analytic hierarchy process–comprehensive index (AHP-CI) model and a logistic regression (LR) model to assess landslide susceptibility (LS). The AHP-CI model is empirically based, with the advantage of being constructible and applicable at various scales without a dataset, though it remains highly subjective. The LR model is a statistical model that requires a training set. The two models represent heuristic and statistical approaches, respectively, to assessing LS. Meanwhile, the basic geological and environmental conditions are considered in the AHP-CI model, while the LR model accounts for the conditions of LUC and urbanization. The results of the multicollinearity diagnostics reflect the rationality of the predisposing factor selection (1.131 < VIF < 4.441). The findings reveal that the AHP-CI model underperforms in LUC and urbanization conditions (AUROC = 0.645, 0.628, and 0.667 for different validation datasets). However, when all the time-varying human activity predisposing factors are considered, the LR model (AUROC = 0.852) performs significantly better under the conditions of solely considering 2010 (AUROC = 0.744) and 2020 (AUROC = 0.810). The CA–Markov model was employed to project the future land use for the short-term (2025), mid-term (2030), and long-term (2040) planning periods. Based on these projections, maps of future LS were created. Importantly, this paper discussed the relationships between landslide management and regional sustainable development under the framework of the UN SDGs, which are relevant to Goal 1, Goal 11, Goal 13, and Goal 15. Finally, this study highlights the importance of integrating strategic land planning, reforestation efforts, and a thorough assessment of human impact predisposing factors with SDG-aligned LS predictions, advocating for a comprehensive, multi-stakeholder strategy to promote sustainable landslide mitigation.

Keywords: landslide susceptibility; land use change; urbanization; CA–Markov model; SDGs; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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