干旱区生态环境知识资源中心(Arid): Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

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DOI	10.1016/j.heliyon.2023.e13287
	Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia
	Tareke, Kassa Abera; Awoke, Admasu Gebeyehu
通讯作者	Tareke, KA
来源期刊	HELIYON
EISSN	2405-8440
出版年	2023
卷号	9 期号:2
英文摘要	The objective of this study is to investigate and perform long-term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.
英文关键词	Artificial neural network Hydrological drought forecasting Linear scaling Regional climate model
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000973000900001
WOS关键词	RIVER-BASIN ; ERROR
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396802
推荐引用方式 GB/T 7714	Tareke, Kassa Abera,Awoke, Admasu Gebeyehu. Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia[J],2023,9(2).
APA	Tareke, Kassa Abera,&Awoke, Admasu Gebeyehu.(2023).Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia.HELIYON,9(2).
MLA	Tareke, Kassa Abera,et al."Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia".HELIYON 9.2(2023).