Journal of Hydrology: Regional Studies Pub Date : 2021-07-06 , DOI: 10.1016/j.ejrh.2021.100855 Habtamu Tamiru 1 , Megersa O. Dinka 2
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Study region
Lower Baro River, Ethiopia.
Study focus
This paper presents the novelty of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo Basin River, Ethiopia. ANN and HEC-RAS model is applied and successfully improves the accuracy of prediction and flood inundation in the region. This study uses 14 meteorological stations on a daily basis for 1999−2005 and 2006−2008 periods, and Topographical Wetness Index (TWI) to the train and test the model respectively. The runoff time series obtained in ANN model is linked to HEC-RAS and the flood depths were generated. The flood inundation generated in HEC-RAS model result was calibrated and validated in Normal Difference Water Index (NDWI).
New hydrological insights for the region
As the inundation map generated from the runoff values of ANN model reveals, the lower Baro river forms huge inundation depth up to 250 cm. The performance the ANN model was evaluated using Nash-Sutcliffe Efficiency (NSE = 0.86), PBIAS = 8.2 % and R2 = 0.91 and NSE = 0.88, PBIAS = 8.5 % and R2 = 0.93 during the training and testing periods respectively. The generated inundation areas in HEC-RAS and the water bodies delineated in NDWI were covered with 94.6 % and 96 % as overlapping areas during the calibration and validation periods respectively. Therefore, it is concluded that the integration of the ANN approach with the HEC-RAS model has improved the prediction accuracy in traditional flood forecasting methods.
中文翻译：
ANN和HEC-RAS模型在埃塞俄比亚巴罗阿科博河流域下游洪水淹没测绘中的应用
研究区域
埃塞俄比亚巴罗河下游。
学习重点
本文介绍了 ANN 和 HEC-RAS 模型在埃塞俄比亚巴罗阿科博盆地下游洪水淹没绘图的新颖性。ANN和HEC-RAS模型的应用，成功提高了该地区预测和洪水淹没的准确性。本研究使用 1999-2005 年和 2006-2008 年期间每天的 14 个气象站，并分别使用地形湿度指数 (TWI) 来训练和测试模型。在 ANN 模型中获得的径流时间序列与 HEC-RAS 相关联，并生成洪水深度。HEC-RAS 模型结果中产生的洪水淹没在正常差异水指数（NDWI）中进行了校准和验证。
该地区的新水文见解
由人工神经网络模型径流值生成的淹没图显示，巴罗河下游形成了高达 250 厘米的巨大淹没深度。ANN 模型的性能 分别在训练和测试期间使用 Nash-Sutcliffe Efficiency (NSE = 0.86)、PBIAS = 8.2 % 和 R 2 = 0.91 和 NSE = 0.88、PBIAS = 8.5 % 和 R 2 = 0.93进行评估。在校准和验证期间，HEC-RAS 中生成的淹没区和 NDWI 中划定的水体分别覆盖了 94.6% 和 96% 作为重叠区域。因此，结论是ANN方法与HEC-RAS模型的结合提高了传统洪水预报方法的预测精度。
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