Assessment of Advanced Artificial Intelligence Techniques for Streamflow Forecasting in Jhelum River Basin

Abstract

| Streamflow forecasting is a crucial hydrological variable. In the current study, the Artificial Intelligence (AI) based techniques: TB (Tree Boost), DTF Decision Tree Forest, SDT Single Decision Tree and conventional Multilayer Perceptron Neural Networks (MLPNN) are used for predicting streamflow of Jhelum River basin. The dataset was divided into two sections, i.e., training dataset (1971-2000); and testing dataset (2001-12). The tendency investigation was done by the Sen’s slope and Mann–Kendall (MK). Decreasing trends annually and seasonally found in MK and Sen’s Slope tests. The highest decreasing trend of -2.23 was observed in Autumn at Narran station, while the lowest change of -0.09 annually observed at Garhi Habibullah station at 95% of the significance level. The flow duration curves (FDCs) of all basin stations showed that DTF performed better and is more effective than other AI techniques. R2, RMSE, and NSE assessed the performance evaluation. DTF was more efficient AI techniques with the average evaluation parameters R2, NSE, and RMSE are 0.998, 0.992, and 382 m3/sec. The assessment revealed that DTF has potential and may be considered as an alternative method for streamflow forecasting.