Analysis of Data Splitting on Streamflow Prediction using Random Forest

Diksha Puri; Parveen Sihag; Mohindra Singh Thakur; Mohammed Jameel; Aaron Anil Chadee; Mohammad Azamathulla Hazi; Diksha Puri; Parveen Sihag; Mohindra Singh Thakur; Mohammed Jameel; Aaron Anil Chadee; Mohammad Azamathulla Hazi

doi:10.3934/environsci.2024029

AIMS Environmental Science

2024, Volume 11, Issue 4: 593-609. doi: 10.3934/environsci.2024029

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Analysis of Data Splitting on Streamflow Prediction using Random Forest

1.
School of Environmental Science, Shoolini University, Solan, Himachal Pradesh, 173229, India (dikshapuri10@gmail.com)
2.
Department of Civil Engineering, Chandigarh University, Punjab 140301, India (parveen12sihag@gmail.com)
3.
Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India (mohindrasinghthakur@shooliniuniversity.com)
4.
Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia (jamoali@kku.edu.sa)
5.
Department of Civil and Environmental Engineering, University of the West Indies, Trinidad

Received: 07 June 2024 Revised: 13 July 2024 Accepted: 17 July 2024 Published: 26 July 2024

This study is focused on the use of random forest (RF) to forecast the streamflow in the Kesinga River basin. A total of 169 data points were gathered monthly for the years 1991–2004 to create a model for streamflow prediction. The dataset was allotted into training and testing stages using various ratios, such as 50/50, 60/40, 70/30, and 80/20. The produced models were evaluated using three statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC). The analysis of the models' performances revealed that the training and testing ratios had a substantial impact on the RF model's predictive abilities; models performed best when the ratio was 60/40. The findings demonstrated the right dataset ratios for precise streamflow prediction, which will be beneficial for hydraulic engineers during the water-related design and engineering stages of water projects.
- streamflow prediction,
- Kesinga basin,
- data separation ratios,
- random forest,
- 60/40
Citation: Diksha Puri, Parveen Sihag, Mohindra Singh Thakur, Mohammed Jameel, Aaron Anil Chadee, Mohammad Azamathulla Hazi. Analysis of Data Splitting on Streamflow Prediction using Random Forest[J]. AIMS Environmental Science, 2024, 11(4): 593-609. doi: 10.3934/environsci.2024029

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Abstract

This study is focused on the use of random forest (RF) to forecast the streamflow in the Kesinga River basin. A total of 169 data points were gathered monthly for the years 1991–2004 to create a model for streamflow prediction. The dataset was allotted into training and testing stages using various ratios, such as 50/50, 60/40, 70/30, and 80/20. The produced models were evaluated using three statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (CC). The analysis of the models' performances revealed that the training and testing ratios had a substantial impact on the RF model's predictive abilities; models performed best when the ratio was 60/40. The findings demonstrated the right dataset ratios for precise streamflow prediction, which will be beneficial for hydraulic engineers during the water-related design and engineering stages of water projects.

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