A Survey on Hybrid Models Used for Hydrological Time-Series Forecasting

Abstract

Accurate forecasting of any time series has always been the top goal for us especially in the field of hydrology as it provides a scientific basis for water resource management, flood control, weather forecasting and plays a chief role in financial and supportable advancement. However, in any case, forecasting of hydrological time series by independent conventional statistical models despite everything stays in trouble on account of time-varying and non-linear attributes of the series. In the light of above, researches have applied machine learning methods especially Neural Networks (NNs) along with traditional forecast methods thus giving rise to a hybrid modeling approach where the former can mine the non-linear relationship in the time series sequence and latter can deal with the linear part of it, thus both complementing each other. In this paper, we present a review of such hybrid models where advantages of both conventional and machine learning methods are incorporated for better real-time forecasting of hydrological time series.