首页> 外文期刊>Journal of Hydrology: Regional Studies >Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia
【24h】

Application of ANN and HEC-RAS model for flood inundation mapping in lower Baro Akobo River Basin, Ethiopia

机译:Ann and HEC-RAS模型在埃塞俄比亚洪水河豚河流域洪水淹没映射的应用

获取原文
           

摘要

Study regionLower Baro River, Ethiopia.Study focusThis 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 regionAs 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.
机译:研究地区波罗河,埃塞俄比亚.Study Focusthis论文提出了在埃塞俄比亚的低音零售山脊河洪水淹没映射的ANN和HEC-RAS模型的新颖性。 ANN和HEC-RAS模型应用并成功提高了该地区预测和洪水淹没的准确性。该研究每天使用14个气象站1999年的1999年?2005年和2006年?2008年的时期,以及火车的地形湿度指数(TWI)分别进行测试。在ANN模型中获得的径流时间序列与HEC-RAS相关联,并产生泛光深度。在HEC-RA模型结果中产生的洪水淹没在正常差异水指数(NDWI)中校准并验证。地区的新水文见解来自ANN模型的径流值产生的淹没地图,下方的Baro河形成了巨大的淹没深度高达250?cm。使用NASH-SUTCLIFFE效率(NSE?= 0.86)评估ANN模型的性能吗?=?8.2%和R2?=?0.91和NSE?=?0.88,PBIAS?=?8.5%和R2?=?培训期间分别为0.93。在校准和验证期间,在NDWI中划分的HEC-RA和NDWI中描绘的水体和96%的水体分别被覆盖为94.6%和96%。因此,得出结论,ANAN方法与HEC-RAS模型的整合提高了传统洪水预测方法的预测准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号