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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| Vydané v: | Sensors Ročník 21; s. 4598 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | Japanese |
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MDPI AG
05.07.2021
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| ISSN: | 1424-8220 |
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| Author | Feras Alasali Zahra Ghanem Fatima Mohammad Rula Tawalbeh Mohammad Alghazzawi |
|---|---|
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| SubjectTerms | 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 |
| Title | 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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