Abstract
Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.
Highlights
Introduction to the Gene ExpressionProgramming (GEP) Model
Multiple Additive Regression Trees (MART) was developed by Derrig and Francis [42] to increase the accuracy of the traditional decision tree model result. e researchers found that the models developed using MART are more accurate models in comparison with any known modeling methodologies. e model can handle categorical and continuous inputs and target variables. e model is more stable due to the use of
Group Methods of Data Handling (GMDH) is a specific type of supervised artificial neural network. e algorithm of GMDH uses the concept of natural selection to control the network size, complexity, and accuracy [45]. e GMDH model starts by selecting a set of functions that showed highest prediction accuracy at previously unseen data
Summary
Ma Shaofu ,1 Anas Mahmood Al-Juboori ,2 Asmaa Hussein Alwan ,3 and Abdel-Salam G. Three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). E models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. ML models such as Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP) are yet to be explored for the generating monthly streamflow time series from annual streamflow time series. E main objective of the current research is to investigate the feasibility of MART, GMDH, and GEP models for generating monthly streamflow time series from annual streamflow time series. GEP was invented by Ferreira as an extension of traditional genetic programming. e program is developed
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