Streamflow prediction using machine learning models in selected rivers of Southern India

The need for adequate data on the spatial and temporal variability of freshwater resources is a significant challenge to the water managers of the world in water resource planning and management. The problems will be acute in the coming years because of the increase in frequency and intensity of hyd...

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Bibliographic Details
Published in:	International journal of river basin management Vol. 22; no. 4; pp. 529 - 555
Main Authors:	Sharma, Rajat Kr, Kumar, Sudhanshu, Padmalal, D., Roy, Arka
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 01-10-2024 Taylor & Francis Ltd
Subjects:	Climate change Climate models Climate prediction Evapotranspiration extreme flow Extreme values Flood management Flood predictions Flow simulation Frequency dependence Freshwater Freshwater resources Groundwater Groundwater data Groundwater levels Hydrologic data Inland water environment Intercomparison Learning algorithms Long short-term memory Machine learning Moisture content Potential evapotranspiration Rainfall Rainfall data Reservoir operation Resource management River basins Rivers Soil moisture Southern India Spatial discrimination Spatial resolution Splines Stream discharge Stream flow Streamflow prediction Support vector machines Temporal variability Temporal variations Water resources Water resources management Water resources planning India
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Summary:	The need for adequate data on the spatial and temporal variability of freshwater resources is a significant challenge to the water managers of the world in water resource planning and management. The problems will be acute in the coming years because of the increase in frequency and intensity of hydrologic extremes due to climate change. Therefore, streamflow prediction has become an important area of research because of its importance in flood mitigation, reservoir operation, and water resource management. In this paper, we have tested four Machine Learning models (ML models): Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Multivariate Adaptive Regression Splines (MARS) for streamflow prediction at daily and monthly time scales in three rivers draining in the different climatic and geological settings. The SVM, RF, LSTM, and MARS models have been trained and tested in the Suvarna, Aghanashini, and Kunderu River Basins in peninsular India. Model intercomparison was made to identify the best suitable model for streamflow prediction. The RF outperforms other models for daily streamflow, and MARS outperforms other models for monthly streamflow prediction in the Suvarna river with Nash-Sutcliffe efficiency (NSE) values of 0.676 and 0.924, respectively. SVM (NSE = 0.741) and RF (NSE = 0.826) are found to be the best models for daily and monthly streamflow prediction in the Aghanashini river. MARS outperformed other models in the case of high, severe, and extreme flow simulation with NSE values of 0.481, 0.374, and 0.455, respectively, in the Aghanashini river. Other hydrological variables (groundwater level data, antecedent soil moisture, potential evapotranspiration data) and a better spatial resolution of rainfall data can be used to develop more accurate machine-learning models for streamflow predictions.
ISSN:	1571-5124 1814-2060
DOI:	10.1080/15715124.2023.2196635