Group method of data handling to forecast the daily water flow at the Cahora Bassa Dam

被引:5
|
作者
Souza, Danilo P. M. [1 ,3 ]
Martinho, Alfeu D. [1 ,3 ]
Rocha, Caio C. [2 ,3 ]
Christo, Eliane da S. [4 ]
Goliatt, Leonardo [1 ,3 ]
机构
[1] Univ Fed Juiz de Fora, Computat Modeling Program, BR-36036900 Juiz de Fora, Minas Gerais, Brazil
[2] Univ Fed Juiz de Fora, Computat Engn Program, BR-36036900 Juiz de Fora, Minas Gerais, Brazil
[3] Univ Fed Juiz de Fora, Rua Jose Lourenco Kelmer, BR-36036900 Juiz De Fora, Minas Gerais, Brazil
[4] Fluminense Fed Univ, Dept Prod Engn, Ave Trabalhadores,Volta Redonda, BR-27255125 Rio De Janeiro, Brazil
关键词
Group Modeling Data Handling; Water flow forecasting; Zambezi Basin; Machine Learning; GMDH ALGORITHM; ZAMBEZI RIVER; CLIMATE; IMPACT;
D O I
10.1007/s11600-022-00834-3
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The Zambezi watershed is essential for water supply, irrigation, fishing activities, and river transport of the populations of Southern Africa. The importance and variability of these water resources make it necessary to develop studies that may help understand and manage them. Despite this need, water resources studies for this region are still scarce. Therefore, the present work aims to present a strategy for forecasting the daily water flow of the Zambezi River in the Cahora Bassa dam, located in Mozambique, an important energy producer in the country and the fourth largest dam in Africa. Historical rainfall, evaporation, and humidity records collected from 2003 to 2011 are used for training and testing a model that forecasts water flow using the Group Method of Data Handling algorithm. The results achieved were compared, through error metrics, with those of other models to prove the effectiveness of the assembled model. They revealed that the proposed model achieves a satisfactory performance for the forecast horizon and could become a helpful tool in monitoring hydrographic basins and forecasting their daily streamflow values.
引用
收藏
页码:1871 / 1883
页数:13
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