Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies
Sedigheh Darabi Cheghabaleki,
Seyed Ehsan Fatemi,
Maryam Hafezparast Mavadat
Abstract:Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results … Show more
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