Machine Learning Approaches for Streamflow Modeling in the Godavari Basin with CMIP6 Dataset

Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in t...

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Bibliographic Details
Published in:Sustainability 2023-08, Vol.15 (16), p.12295
Main Authors: Saravanan, Subbarayan, Reddy, Nagireddy Masthan, Pham, Quoc Bao, Alodah, Abdullah, Abdo, Hazem Ghassan, Almohamad, Hussein, Al Dughairi, Ahmed Abdullah
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Climate change
Datasets
Drinking water
Floods
Forecasting
Hydrology
India
Machine learning
Management
Neural networks
Precipitation
Rain
Random variables
Rivers
Runoff
Stream flow
Streamflow
Support vector machines
Topography
Water
Water use
Watersheds
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Summary:Accurate streamflow modeling is crucial for effective water resource management. This study used five machine learning models (support vector regressor (SVR), random forest (RF), M5-pruned model (M5P), multilayer perceptron (MLP), and linear regression (LR)) to simulate one-day-ahead streamflow in the Pranhita subbasin (Godavari basin), India, from 1993 to 2014. Input parameters were selected using correlation and pairwise correlation attribution evaluation methods, incorporating a two-day lag of streamflow, maximum and minimum temperatures, and various precipitation datasets (including Indian Meteorological Department (IMD), EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). Bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets were utilized in the modeling process. Model performance was evaluated using Pearson correlation (R), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). IMD outperformed all CMIP6 datasets in streamflow modeling, while RF demonstrated the best performance among the developed models for both CMIP6 and IMD datasets. During the training phase, RF exhibited NSE, R, R2, and RMSE values of 0.95, 0.979, 0.937, and 30.805 m3/s, respectively, using IMD gridded precipitation as input. In the testing phase, the corresponding values were 0.681, 0.91, 0.828, and 41.237 m3/s. The results highlight the significance of advanced machine learning models in streamflow modeling applications, providing valuable insights for water resource management and decision making.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151612295