A Comprehensive Review of Enhancing Flooding Prediction Accuracy Using Hybrid Deep Learning Algorithms

Yusuf Yunusa, Kabiru Ibrahim Musa, Fatima Umar Zambuk, Abuzairu Ahmad, Datti Useni Emmanuel

Abstract


Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage to properties. A number of cities are subject to monsoon influences and hence face the disaster almost every year. Early notification of flood incidents could benefit the authorities and public in devising both short and long-term preventive measures, to prepare evacuation and rescue missions, and to relieve the flood victims. However, flood forecasting is often considered a regression problem, where the goal is to predict a continuous variable rather than classify events into discrete classes. This work is aimed at developing a model using hybrid Model using a shared MLP and LSTM algorithm combining the advantages of MLP and Long short-term memory (LSTM) to predict flooding with high accuracy. This research can assist decision makers in anticipating potential flooded areas and preemptively taking measures to mitigate socioeconomic disruptions caused by urban flooding, researchers and practitioners have focused on building accurate real‐time flood prediction models. This work is based on the basic idea of hybridizing deep learning technique to correctly predict flooding. The propose model will make used of rainfall datasets collected from open-source repository portal collected from Nigerian data portal. The daily rainfall data for Kebbi state for eighteen years, from 2005 to 2022, was successfully acquired from the Nigerian data portal.  The rainfall data consists of (12,053 data points at daily time steps). The research implementation is carried out in MATLAB 2021a.


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