Prediction of flow rate in rivers is essential for the planning and management of water resources. This study shows that, based on a Machine Learning approach, accurate models for streamflow prediction can be developed. The Des Moines watershed, which includes both Des Moines River and Raccoon River, was chosen as a case study. Only the daily river discharge was considered for the modeling of 10 stations located on different tributaries of the two rivers. Four machine learning algorithms were applied for the streamflow prediction: Random Subspace, M5P, Random Forest and Bagging. The performance of applied algorithms was assessed using statistical performance indicators and graphical representations. Three stations were selected for the training and testing of the different Machine Learning models. Then, the best model was validated on the other seven stations. Prediction accuracy was also assessed as the forecast horizon increased. Overall, M5P algorithms led to the best predictions, with R2 equal to 0.970 and 0.960 for the stations of East Fork Des Moines River at Dakota City and Des Moines River near Tracy, respectively. Accurate predictions were obtained also on the Raccoon River, with R2 equal to 0.938 and 0.887 for the stations of North Raccoon River near Jefferson and Raccoon River at Van Meter, respectively.

River flow rate prediction in the Des Moines watershed (Iowa, USA): a machine learning approach

Di Nunno F.
;
de Marinis G.;Granata F.
2022

Abstract

Prediction of flow rate in rivers is essential for the planning and management of water resources. This study shows that, based on a Machine Learning approach, accurate models for streamflow prediction can be developed. The Des Moines watershed, which includes both Des Moines River and Raccoon River, was chosen as a case study. Only the daily river discharge was considered for the modeling of 10 stations located on different tributaries of the two rivers. Four machine learning algorithms were applied for the streamflow prediction: Random Subspace, M5P, Random Forest and Bagging. The performance of applied algorithms was assessed using statistical performance indicators and graphical representations. Three stations were selected for the training and testing of the different Machine Learning models. Then, the best model was validated on the other seven stations. Prediction accuracy was also assessed as the forecast horizon increased. Overall, M5P algorithms led to the best predictions, with R2 equal to 0.970 and 0.960 for the stations of East Fork Des Moines River at Dakota City and Des Moines River near Tracy, respectively. Accurate predictions were obtained also on the Raccoon River, with R2 equal to 0.938 and 0.887 for the stations of North Raccoon River near Jefferson and Raccoon River at Van Meter, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/91647
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