Analysis of the Dominance of Natural Factors and Human Activities on Recurrent Floods on Sumatra Island Using SHAP-Based Random Forest - Sudin saepudin; April Lia Hananto; Ibnu Mu'ti Lidinillah Abidin; Muhamad Rivqi Nurridwan; Muhamad Zidane Mustofa; Hidayat Abdul Wahab; Difa Nafis Conoras Algifari (Scientific Journal of Informatics, 2026)
Purpose: The objectives of this work are to conduct a comprehensive analysis of the primary influences of both natural and anthropogenic causes on flooding in Kalimantan, utilising a transparent machine learning approach, and to provide policymakers with valuable insights for formulating strategies to mitigate flooding. Methods: The research employs the Random Forest model, integrating SHAP (Shapley Additive Explanations), to examine non-linear multivariate correlations and ascertain the significance of each variable. The data consists of both natural elements (precipitation, elevation, slope gradient, and proximity to the river) and anthropogenic activity (land fire hotspots, planting, mining, and building new infrastructure). The data also includes public sentiment data in text form. We got these data points per year from 2021 to 2025. We used R-squared and SHAP scores to figure out how accurate the model was. Result: The model has a high R² score of 0.81, which shows that it can make accurate predictions. Using SHAP (SHapley Additive exPlanations), we can see that natural factors, such as how far away the river is and how much it rains, are what make the area vulnerable in the first place. Human actions, on the other hand, are what cause the floods to happen again and again. The indicator for hotspots of land burning is the most important factor. Plantation and mining operations make up more than 90% of the overall contributions, followed by other predictors, in the case of flood-induced deforestation. Novelty: The current study proposes a framework for explicable AI that integrates the use of random forests and SHAP to assess the significance of various flood risk indicators via quantitative analysis of geographical and public opinion data.