ITM Web Conf.
Volume 58, 2024The 6th IndoMS International Conference on Mathematics and Applications (The 6th IICMA 2023)
|Number of page(s)
|09 January 2024
Modeling the Flood Disaster in South Kalimantan Using Geographically Weighted Regression and Mixed Geographically Weighted Regression
Department of Mathematics, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, East Java, Indonesia
* Corresponding author: email@example.com
The flood disaster in South Kalimantan is a crucial problem that needs to be addressed because the impact is relatively severe. So, this study aims to model flood disasters in South Kalimantan based on factors suspected to be the cause, including population density, rainfall, residential area, and forest area. This study uses two methods of spatial statistics, namely the Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) methods. The weighting used is Adaptive Gaussian. The modeling results show that the GWR model is superior in explaining the causes of flood events in South Kalimantan, which is indicated by the highest coefficient of determination value of 95.62% compared to the regression and MGWR models. Nonetheless, the MGWR model can explain the causes of flooding in Kalimantan. The GWR and MGWR models show that the area that is vulnerable to flooding is Balangan District. The results of this study contribute to providing alternative information for disaster mitigation to minimize losses.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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