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

Saved in:

Bibliographic Details
Published in:	Sensors Vol. 21; p. 4598
Main Authors:	Feras Alasali, Rula Tawalbeh, Zahra Ghanem, Fatima Mohammad, Mohammad Alghazzawi
Format:	Journal Article
Language:	Japanese
Published:	MDPI AG 05.07.2021
Subjects:	ANN Artificial neural network Atmospheric Science Chemical technology Computer science Data mining Disasters Early warning system Earth and Planetary Sciences Environmental Engineering Environmental Science Flash flood flash floods Flood Inundation Modeling Flood myth Floods Forecasting FOS: Environmental engineering FOS: Philosophy, ethics and religion Global and Planetary Change Global Flood Risk Assessment and Management Groundwater Level Forecasting Hydrological Modeling using Machine Learning Methods Jordan Machine learning Minification Neural Networks, Computer Philosophy Physical Sciences Predictive modelling Programming language Rainfall-Runoff Modeling real-time early warning remote sensing rolling forecast Satellite-Based Precipitation Estimation and Validation sustainable monitoring system Telecommunications Theology TP1-1185 Urban Flooding Warning system Water
ISSN:	1424-8220
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!