Improving the performance of evidential belief function model using five machine learning models in earth fissure susceptibility mapping

The frequency and occurrence of earth fissures threaten human settlements, infrastructure, and agricultural lands in many parts of the globe. This natural hazard has particularly posed great challenge in the North, Central, and Eastern parts of the Najaf Abad Basin of Iran. This paper presents a comparative analysis of machine learning algorithms for earth fissure susceptibility assessment, and involves the creation of an earth fissure database by selecting and validating 200 fissure locations. To improve the accuracy of the fissure locations, various geographic data sources, such as Google Earth images, field surveys, and GPS measurements, were employed. The locations were divided into training and validation sets based on sixteen conditioning factors for finding the earth fissure susceptible zones using EBF (Evidential belief function), EBF - Multilayer Perceptron (MLP), EBF - Function Tree (FT), EBF - Alternating decision tree (ADtree), EBF - Adaptive Boosting (AdaBoost), EBF - Random SubSpace (RSS), EBF - Naive Bayes Tree (NBTree) models. The results show that earth fissures occur in greater concentrations where groundwater levels are lowest, and their prevalence is closely associated to both agricultural activities and the presence of clay, clayey sand, and silty clay soils. Susceptibility analysis results revealed that 15-21 % agricultural dependent population falls under the very high susceptibility risk zone in and around the major cities of the Najaf Abad region. The receiver operating characteristic (ROC) curve analysis showed that the EBF-RSS algorithm has higher accuracy (>99 %) compared to other algorithms (>85 %) in training and validation phases for earth fissure susceptibility prediction. Our findings emphasise the need to prioritise these susceptible areas for sustainable land and water management strategies and implement effective measures to mitigate the impact of earth fissures. Our study also advances our knowledge on geospatial analysis and earth fissures-related hazard assessment by applying five machine learning algorithms that shows spatial similarity in susceptibility categories with splendid accuracy performance. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.