A Sustainable Early Warning System Using Rolling Forecasts Based on ANN and Golden Ratio Optimization Methods to Accurately Predict Real-Time Water Levels and Flash Flood

Abstract

Remote monitoring sensor systems play a significant role in the evaluation and minimization of natural disasters and risk. This article presents a sustainable and real-time early warning system of sensors employed in flash flood prediction by using a rolling forecast model based on Artificial Neural Network (ANN) and Golden Ratio Optimization (GROM) methods. This Early Flood Warning System (EFWS) aims to support decision makers by providing reliable and accurate information and warning about any possible flood events within an efficient lead-time to reduce any damages due to flash floods. In this work, to improve the performance of the EFWS, an ANN forecast model based on a new optimization method, GROM, is developed and compared to the traditional ANN model. Furthermore, due to the lack of literature regarding the optimal ANN structural model for forecasting the flash flood, this paper is one of the first extensive investigations into the impact of using different exogenous variables and parameters on the ANN structure. The effect of using a rolling forecast model compared to fixed model on the accuracy of the forecasts is investigated as well. The results indicate that the rolling ANN forecast model based on GROM successfully improved the model accuracy by 40% compared to the traditional ANN model and by 93.5% compared to the fixed forecast model.

Innovative Characterization and Comparative Analysis of Water Level Sensors for Enhanced Early Detection and Warning of Floods

“…In this section, a few of these studies are listed, as well as studies regarding other applications. Alasali et al [7] used an off-the-shelf ultrasonic sensor (HC-SR04) in a real-time early-warning system based on an artificial neural network and golden ratio optimization that was employed to predict flash floods to alert decision-makers and users. The system was intended for installation in the center of Amman.…”

Development of an Artificial Neural Network Algorithm Embedded in an On-Site Sensor for Water Level Forecasting

“…The ELU function is also similar to the rectified linear unit (ReLU), with a difference in output value for negative values of input. A three-layer network structure with one input layer, one hidden layer, and one output layer is adopted because of the limited computing power of the RPi 3 Model B+ module in the sensor [ 27 ]. No general guidelines exist for specifying the optimal number of nodes required in the hidden layer [ 34 ].…”

“…No extra calculation was performed on the sensors in these studies. Other studies used microcontroller-based sensors to collect environmental information and perform calculations in cloud-based neural networks to predict flood disaster conditions [ 25 , 26 , 27 ]. These studies confirmed the functionality of ANN models for water level predictions.…”

Mitigation and public coordination for Flood Disaster Risk Reduction (FDRR) in the implementation of North Luwu sustainable development

“…26 of 2007 concerning Spatial Planning. This of course must be accompanied by knowledge, in addition to data and information about disasters by the community and the government as policyholders in the implementation of development [4]. The government and society, which are part of a community, must of course be interrelated, coordinate, and work together in realizing sustainable development.…”