Streamflow forecasting is one of the most crucial exercises for reservoir operations, hydropower generation, and irrigation scheduling management. Conventionally, it is done by developing a forecasting model based on historical records of various variables. Broadly, the models can be divided into either physically based mathematical models or data-driven “black box” models. To understand the hydrologic system, the development of physically based models necessitates a vast quantity of diverse data connected to numerous physical processes, which is typically challenging to gather under actual field conditions, especially in developing nations. On the other hand, data-driven models do not require system expertise, and as a result, they have gained popularity, particularly with improvements in artificial intelligence technology. Stochastic time series models have historically been employed to predict streamflow. Because Artificial Neural Network (ANN) models forecast streamflow more accurately than time series models did in the 1990s, they became increasingly popular. Artificial intelligence (AI), an offshoot of computer science, can analyse long-series and large-scale hydrological data. In recent years, applying AI technology to hydrological forecasting modelling has been one of the front-burner issues. Streamflow forecasting also uses various AI techniques like SVM, Fuzzy Logic, GP, GEP, etc. Later, by merging them with other models, the performance of the current AI-based data-driven models is improved. This chapter discusses various AI-based models, their basic concepts, types, and applications for forecasting streamflow.
