Abstract
Flood is one of the prominent climate-induced disasters (CIDs), which causes enormous damage, financial losses, and casualties every year across the world. The intensities and damages of floods are prone to change due to future climate scenarios. In order to analyze the impact of climate change on flood patterns in the Maharashtra state of India, this research employs machine learning models to analyze historical data and project future floods. In this work, we have defined and used 20 weather parameters based on temperature and precipitation records collected from the India Meteorological Department (IMD) and then to simulate and predict the floods in Maharashtra state. Then we use these parameters to build the machine learning models such as Artificial Neural Network (ANN), Light Gradient-Boosting Machines (LightGBM), and Least Squares Support Vector Machines (LSSVM) for estimating the approximate number of occurrences of floods till 2100 on different shared socioeconomic pathways (SSPs) scenarios. Based on our simulation experiments for data analytics, we found that LightGBM performed the best in the validation phase giving an F1-score of 0.895 and an AUC-ROC score of 0.863. Furthermore, we also used LightGBM for simulations of future scenarios in Maharashtra state. This work introduces a novel approach to predict climate-induced disasters (CIDs), floods in this case, by utilizing data from past disasters, global climate models, and climate change measurements. We believe that the proposed model can be utilized to analyse the impacts of climate change on floods in Maharashtra state and subsequently help the local government bodies and disaster management authorities to plan and prepare accordingly.
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Vage, S., Gupta, T., Roy, S. (2023). Impact Analysis of Climate Change on Floods in an Indian Region Using Machine Learning. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14261. Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_31
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