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- Title
Flood warning system using internet of things, artificial intelligence and hydraulic modeling (case study: Behesht-Abad Watershed, Iran).
- Authors
Ghanbari, Ahmad; Tahmasebipour, Nasser; Zeinivand, Hossein; Heidari, Majid Ibn Ali; Abdollahi, Sajjad
- Abstract
Flood is a sudden event with the possibility of life and financial risks, and recently innovative technologies can help to increase the accuracy of its warning. In this research, which was conducted in the Behesht-Abad watershed located in Chaharmahal and Bakhtiari province in Iran, the integration of the Internet of Things, the hybrid of wavelet neural network (WANN) and the HEC-RAS 2D hydraulic models have been used for developing a flood warning system. The ten-minute streamflow data of mentioned river at three hydrometric stations as well as simultaneous information of precipitation and temperature of two meteorological stations were used. The target interval of river was divided into two parts, (i) Upstream and (ii) Downstream (floodplain) intervals. The floods at the upstream and downstream intervals were modeled using the WANN and HEC-RAS 2D models, respectively. The predicted flood discharge using WANN model at the end of upstream interval produces the main input of HEC-RAS 2D model for flood zoning at the downstream interval. The performance of WANN model for predicting discharges entering the floodplain was very well based on R2 and RMSE criteria equal to 0.9996 and 0.6217 for the training period and 0.9992 and 0.2890 for the validation periods, respectively. The results show that this warning system can effectively estimate the flood discharge in short-term intervals with temporal prediction accuracy of about a minute and a spatial prediction based on cell size of 0.2 m and send it to user's mobile phone through the Internet.
- Subjects
IRAN; FLOOD warning systems; ARTIFICIAL intelligence; INTERNET of things; HYDRAULIC models; WATERSHEDS
- Publication
Acta Geophysica, 2024, Vol 72, Issue 4, p2815
- ISSN
1895-6572
- Publication type
Article
- DOI
10.1007/s11600-023-01174-6