IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM Network

Abstract

Floods in recent years have frequently resulted in environmental, economic, as well as loss of human life. People are less aware of incoming floods if there is no early warning system. This proposal outlines the design of a monitoring system to obtain real-time data on rain gauge and water level. The monitoring system is based on IoT via a GSM network to provide real-time data cloud and dashboard display on Grafana platform. The rainfall forecasting model used Long Short-Term Memory (LSTM) networks to predict future rainfall and water level values which could cause floods. The result was experimented with using historical data since the current data of the monitoring system is insufficient yet to make an accurate prediction. The main findings of the research are the predicted values of streamflow and rainfall for historical data, also water level and rain gauge for new data. The primary result was experimented with using historical data on two rainfall stations and one streamflow. Also, the primary result was experimented with using new data on two water level stations and one rainfall. The forecasting method that applied LSTM showed high accuracy of the result reaching more than 90%. Based on these results, the system can be used as a non-structural solution to alleviate the damage caused by urban floods.