Next Article in Journal
Thermal and Hydrodynamic Changes under a Warmer Climate in a Variably Stratified Hypereutrophic Reservoir
Next Article in Special Issue
Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence
Previous Article in Journal
How Environmental Protection Motivation Influences on Residents’ Recycled Water Reuse Behaviors: A Case Study in Xi’an City
Previous Article in Special Issue
New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling
Open AccessArticle

Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models

1
Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan
2
Water Resources and Climatic Change Research Centre, National Hydraulic Research Institute of Malaysia, Selangor 43300, Malaysia
3
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
*
Authors to whom correspondence should be addressed.
Water 2018, 10(9), 1283; https://doi.org/10.3390/w10091283
Received: 4 July 2018 / Revised: 30 August 2018 / Accepted: 13 September 2018 / Published: 19 September 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
A regional inundation early warning system is crucial to alleviating flood risks and reducing loss of life and property. This study aims to provide real-time multi-step-ahead forecasting of flood inundation maps during storm events for flood early warnings in inundation-prone regions. For decades, the Kemaman River Basin, located on the east coast of the West Malaysia Peninsular, has suffered from monsoon floods that have caused serious damage. The downstream region with an area of approximately 100 km2 located on the east side of this basin is selected as the study area. We explore and implement a hybrid ANN-based regional flood inundation forecast system in the study area. The system combines two popular artificial neural networks—the self-organizing map (SOM) and the recurrent nonlinear autoregressive with exogenous inputs (RNARX)—to sequentially produce regional flood inundation maps during storm events. The results show that: (1) the 4 × 4 SOM network can effectively cluster regional inundation depths; (2) RNARX networks can accurately forecast the long-term (3–12 h) regional average inundation depths; and (3) the hybrid models can produce adequate real-time regional flood inundation maps. The proposed ANN-based model was shown to very quickly carry out multi-step-ahead forecasting of area-wide inundation depths with sufficient lead time (up to 12 h) and can visualize the forecasted results on Google Earth using user devices to help decision makers and residents take precautionary measures against flooding. View Full-Text
Keywords: ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX) ANN-based models; flood inundation map; self-organizing map (SOM); recurrent nonlinear autoregressive with exogenous inputs (RNARX)
Show Figures

Figure 1

MDPI and ACS Style

Chang, L.-C.; Amin, M.Z.M.; Yang, S.-N.; Chang, F.-J. Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models. Water 2018, 10, 1283.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop