Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Mohammed Moishin,Ravinesh C Deo,Ramendra Prasad,Nawin Raj,Shahab Abdulla

doi:10.1109/access.2021.3065939

Abstract

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index ( I F ), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged I F , improved by antecedent and real-time rainfall data to forecast the next daily I F value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting I F and its potential use in disaster management and risk mitigation in the current phase of extreme weather events.

Highlights

Detection of natural disasters such as floods can greatly assist humans in reducing the extent of the damage caused by such events
There have been improvements in early warning systems since many other emerging technologies, which are somewhat constrained in developing nations, have strong potential to deliver
The authors hope to develop and validate a new flood forecasting model that can be used to mitigate the impact of floods in island nations and elsewhere by enabling the people and organizations to be better prepared for future flood events

Summary

Introduction

Detection of natural disasters such as floods can greatly assist humans in reducing the extent of the damage caused by such events. One simple, yet a robust mathematical tool used to determine the flood state at a particular time for a given area is the Flood Index (IF ) [3]. This approach represents the standardized form of ‘Effective Precipitation’ (PE ) based on the rationale that a flood event on any particular day is dependent on the current and the previous day’s precipitation with the effect of previous day’s precipitation on current day’s flood state gradually reducing due to the effect of hydrological factors [4]. As a flood monitoring index, IF cannot be currently used to determine

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