Hybrid computational model of reflectivity values and rain cell size for improved flood disaster prediction: raw data reading process implementation - Hassan, Md Tanvir; Basri, Atikah Balqis; Badron, Khairayu; Handayani, Dini Oktarina Dwi; Mahmod Attar Bashi, Zainab Senan (IADITI - International Association for Digital Transformation and Technological Innovation, 2025)
Climate change effects in Malaysia have been evident over the past few years, with the nation experiencing devastating floods in regions such as Pahang, Johor, Kelantan, and Terengganu. These deadly floods, occurring during the monsoon season, are a direct consequence of global climate change. This research project aims to develop a capability for flood disaster prediction in Malaysia, which has been facing extreme weather. The specific focus of this study is on a preliminary method to read radar data, a crucial component for accurate flood forecasting. The research involves designing a hybrid model that incorporates both radar reflectivity and rain cell size, in conjunction with current flood forecasting models, to improve flood prediction accuracy. The model will utilize radar data and rain gauge data from 2018-2023 to derive parameters essential for simulation purposes. This preliminary method of reading and integrating radar data is expected to enhance the flood prediction model's reliability. The raw data reading shows the size of the cloud and the reading of the reflectivity value.