Comparison of Landslide Susceptibility Maps using Logistic Regression (LR) and Generalized Additive Model (GAM)

Yansari, Zeinab Timoori; Hosseinzadeh, Seyed Reza; Kavian, Ataollah; Pourghasemi, Hamid Reza

doi:10.29252/jwmr.9.18.208

Cited by 4 publications

(1 citation statement)

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“…The µ indicates the predictor variable category relationship that is unaffected by nonlinear transformation, while the ε represents the residual. In a traditional GAM network, a feature was made to have monotonically increasing or decreasing properties on the target value by setting constraints and penalty functions or adjusting the number of spline functions [41]. The GAM model are more explanatory than the other black box ML models; they could not be represented as a single function describing the estimated relationship between independent and dependent variables.…”

Section: Gami-netmentioning

confidence: 99%

A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence

et al. 2023

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Landslides are a common and costly geological hazard, with regular occurrences leading to significant damage and losses. To effectively manage land use and reduce the risk of landslides, it is crucial to conduct susceptibility assessments. To date, many machine-learning methods have been applied to the landslide susceptibility map (LSM). However, as a risk prediction, landslide susceptibility without good interpretability would be a risky approach to apply these methods to real life. This study aimed to assess the LSM in the region of Nayong in Guizhou, China, and conduct a comprehensive assessment and evaluation of landslide susceptibility maps utilizing an explainable artificial intelligence. This study incorporates remote sensing data, field surveys, geographic information system techniques, and interpretable machine-learning techniques to analyze the sensitivity to landslides and to contrast it with other conventional models. As an interpretable machine-learning method, generalized additive models with structured interactions (GAMI-net) could be used to understand how LSM models make decisions. The results showed that the GAMI-net model was valid and had an area under curve (AUC) value of 0.91 on the receiver operating characteristic (ROC) curve, which is better than the values of 0.85 and 0.81 for the random forest and SVM models, respectively. The coal mining, rock desertification, and rainfall greater than 1300 mm were more susceptible to landslides in the study area. Additionally, the pairwise interaction factors, such as rainfall and mining, lithology and rainfall, and rainfall and elevation, also increased the landslide susceptibility. The results showed that interpretable models could accurately predict landslide susceptibility and reveal the causes of landslide occurrence. The GAMI-net-based model exhibited good predictive capability and significantly increased model interpretability to inform landslide management and decision making, which suggests its great potential for application in LSM.

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Section: Gami-netmentioning

confidence: 99%