Monthly Streamflow Prediction by Metaheuristic Regression Approaches Considering Satellite Precipitation Data

• Published in: Water (Basel) Vol. 14; no. 22; p. 3636
• Main Authors: Mojtaba Mehraein, Aadhityaa Mohanavelu, Sujay Raghavendra Naganna, Christoph Kulls, Ozgur Kisi
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-11-2022
• Subjects: metaheuristic regression approaches; satellite precipitation data; streamflow prediction; TRMM
• ISSN: 2073-4441
• DOI: 10.3390/w14223636

In this study, the viability of three metaheuristic regression techniques, CatBoost (CB), random forest (RF) and extreme gradient tree boosting (XGBoost, XGB), is investigated for the prediction of monthly streamflow considering satellite precipitation data. Monthly streamflow data from three measuring stations in Turkey and satellite rainfall data derived from Tropical Rainfall Measuring Mission (TRMM) were used as inputs to the models to predict 1 month ahead streamflow. Such predictions are crucial for decision-making in water resource planning and management associated with water allocations, water market planning, restricting water supply and managing drought. The outcomes of the metaheuristic regression methods were compared with those of artificial neural networks (ANN) and nonlinear regression (NLR). The effect of the periodicity component was also investigated by importing the month number of the streamflow data as input. In the first part of the study, the streamflow at each station was predicted using CB, RF, XGB, ANN and NLR methods and considering TRMM data. In the second part, streamflow at the downstream station was predicted using data from upstream stations. In both parts, the CB and XGB methods generally provided similar accuracy and performed superior to the RF, ANN and NLR methods. It was observed that the use of TRMM rainfall data and the periodicity component considerably improved the efficiency of the metaheuristic regression methods in modeling (prediction) streamflow. The use of TRMM data as inputs improved the root mean square error (RMSE) of CB, RF and XGB by 36%, 31% and 24%, respectively, on average, while the corresponding values were 37%, 18% and 43% after introducing periodicity information into the model’s inputs.