Assessment of machine learning models to predict daily streamflow in a semiarid river catchment

In this study, we employ explainable machine learning (ML) models to predict daily streamflow ( \(Q_{\rm{flow}}\) $Q_{\mathrm{flow}}$ ) by leveraging hydro-meteorological parameters. The predictive matrix incorporates crucial factors such as daily rainfall, temperature, relative humidity, solar radiation, wind speed, and the one-day lag value of \(Q_{\rm{flow}}\) $Q_{\mathrm{flow}}$ . Notably, among these parameters, the one-day lag value of \(Q_{\rm{flow}}\) $Q_{\mathrm{flow}}$ , along with rainfall, solar radiation, temperature, and relative humidity emerge as highly influential predictors. We apply various ML models, including bagging ensemble learning, boosting ensemble learning, Gaussian process regression (GPR), and automated machine learning (Auto ML). Following a rigorous evaluation, the bagging ensemble learning model stands out as the most effective with a correlation coefficient (R = 0.80) and root-mean-square error (RMSE = 218). Further, we compare the \(Q_{\rm{flow}}\) $Q_{\mathrm{flow}}$ predicted using ML models with a process-based hydrological model (SWAT) that was executed using a similar set of climatic variables as the input parameters. In our case, the predictive strength of the ML model (R = 0.80; RMSE = 218) to estimate ( \(Q_{\rm{flow}}\) $Q_{\mathrm{flow}}$ ) is greater than the SWAT (R = 0.82; RMSE = 281). In conclusion, by emphasizing the importance of explainable ML models and highlighting the significance of specific hydro-meteorological parameters, our study contributes to advancing the field of hydrology and water resource management.