Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Real-Time Flood Analysis Using Artificial Neural Network
https://orcid.org/https://orcid.org/0000-0002-0053-1210
https://orcid.org/https://orcid.org/0000-0001-8480-2227
Floods are natural disasters that affect the likelihood of occurrence. Forecasting and predicting floods have a significant role to play in ensuring that mitigation, adequate planning, and management can be carried out in advance. The Artificial Neural Network (ANN) is one of the techniques by which rapid forecasting and prediction can be carried out. In the present study, ANN has been used to simulate real-time floods in the lower Tapi basin. Data from the upstream gauging station of the reservoir, the inflow of the reservoir, and the downstream gauging site were simulated for three different events. The Feed–Forward network, the Levenberg Marquardt learning rule, and the Sigmoidal Axon transfer function are used in the models. Developed models have a correlation coefficient value close to one. The findings acquired from these models are satisfactory and the predicted flood discharge of the ANN is consistent with the observed values.
Real-Time Flood Analysis Using Artificial Neural Network
https://orcid.org/https://orcid.org/0000-0002-0053-1210
https://orcid.org/https://orcid.org/0000-0001-8480-2227
Floods are natural disasters that affect the likelihood of occurrence. Forecasting and predicting floods have a significant role to play in ensuring that mitigation, adequate planning, and management can be carried out in advance. The Artificial Neural Network (ANN) is one of the techniques by which rapid forecasting and prediction can be carried out. In the present study, ANN has been used to simulate real-time floods in the lower Tapi basin. Data from the upstream gauging station of the reservoir, the inflow of the reservoir, and the downstream gauging site were simulated for three different events. The Feed–Forward network, the Levenberg Marquardt learning rule, and the Sigmoidal Axon transfer function are used in the models. Developed models have a correlation coefficient value close to one. The findings acquired from these models are satisfactory and the predicted flood discharge of the ANN is consistent with the observed values.
Real-Time Flood Analysis Using Artificial Neural Network
https://orcid.org/https://orcid.org/0000-0002-0053-1210
https://orcid.org/https://orcid.org/0000-0001-8480-2227
Floods are natural disasters that affect the likelihood of occurrence. Forecasting and predicting floods have a significant role to play in ensuring that mitigation, adequate planning, and management can be carried out in advance. The Artificial Neural Network (ANN) is one of the techniques by which rapid forecasting and prediction can be carried out. In the present study, ANN has been used to simulate real-time floods in the lower Tapi basin. Data from the upstream gauging station of the reservoir, the inflow of the reservoir, and the downstream gauging site were simulated for three different events. The Feed–Forward network, the Levenberg Marquardt learning rule, and the Sigmoidal Axon transfer function are used in the models. Developed models have a correlation coefficient value close to one. The findings acquired from these models are satisfactory and the predicted flood discharge of the ANN is consistent with the observed values.
Real-Time Flood Analysis Using Artificial Neural Network
Lecture Notes in Civil Engineering
Pathak, K. K. (Herausgeber:in) / Bandara, J. M. S. J. (Herausgeber:in) / Agrawal, Ramakant (Herausgeber:in) / Kumar, Vijendra (Autor:in) / Yadav, S. M. (Autor:in)
28.09.2020
14 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Flood , Forecasting , Prediction , Artificial neural network , Hidden layer , Feed-forward network Engineering , Building Construction and Design , Geotechnical Engineering & Applied Earth Sciences , Transportation Technology and Traffic Engineering , Geoengineering, Foundations, Hydraulics , Construction Management , Remote Sensing/Photogrammetry
Real-Time Flood Analysis Using Artificial Neural Network
| TIBKAT | 2021
Real-Time Flood Forecasting Using Neural Networks
| British Library Online Contents | 1998
Real-Time Flood Forecasting Using Neural Networks
| Online Contents | 1998
Real‐Time Flood Forecasting Using Neural Networks
| Wiley | 1998
Improving flood forecasting in Bangladesh using an artificial neural network
| British Library Online Contents | 2010