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

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Veröffentlicht in:Sensors Jg. 21; S. 4598
Hauptverfasser: Feras Alasali, Rula Tawalbeh, Zahra Ghanem, Fatima Mohammad, Mohammad Alghazzawi
Format: Journal Article
Sprache:Japanisch
Veröffentlicht: MDPI AG 05.07.2021
Schlagworte:
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-Zugang:Volltext
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ISSN:1424-8220
DOI:10.60692/4q1hp-1jt74