Constraint Free Early Warning System for Flood Using Multivariate LSTM Network

Abstract

Floods are the world's most damaging natural disasters, which not only claim thousands of human lives but also result in huge damage to infrastructure. Floods if forecasted in advance can help in the reduction of damages. Flood prediction especially long term is a complex task as it involves many hydrological and metrological parameters. For the short and medium-term, machine learning methods seem to have contributed to a great extent in simulating mathematical modelling of the physical flow processes of floods. However, these developed model's performance lacks generalization. Such systems trained on one geographical location's data have degraded performance when exploited for another location. In this paper, Long Short-Term Memory (LSTM) machine learning algorithm was applied where the hourly river level, river flow, and rainfall data from Brooklyn station was used as input data to the model and test for one hour, two hours, four hours, six hours, eight hours, and twelve hours in advance for river level prediction at Hoppers Crossing station. The developed algorithm achieved an accuracy of 98% for one hour and 97.2 %, 96.14 %, 94.67%,94.61 %, and 93.55% for two, four, six, eight, and twelve hours respectively. These systems not only forecast the future water level but also help in estimating the water level in case of a sensor failure. Multivariate modelling is utilized to predict the unknown parameter from the given other parametric values, thus not only predicting the forecasted water level but also reporting the sensor failure.