Accurate runoff forecasting plays a key role in catchment water management and water
resources system planning. To improve the prediction accuracy, one needs to strive to …

Research within the field of hydrology often focuses on the statistical problem of comparing
stochastic to machine learning (ML) forecasting methods. The performed comparisons are …

Time series modeling is necessary for the planning and management of reservoirs. More
recently, the soft computing techniques have been used in hydrological modeling and …

In this paper, the computer aided numerical method for hydrological model calibration is
reviewed. The content includes review of the watershed hydrological models (data-driven …

River flow forecasting is an essential procedure that is necessary for proper reservoir
operation. Accurate forecasting results in good control of water availability, refined operation …

Streamflow forecasting is of great importance to water resources management and flood
defense. On the other hand, a better understanding of the streamflow process is …

We provide contingent empirical evidence on the solutions to three problems associated
with univariate time series forecasting using machine learning (ML) algorithms by …

Many hydrological applications related to water resource planning and management
primarily rely on a succession of streamflow forecasts with extensive lead times. In this study …

We assess the performance of the recently introduced Prophet model in multi-step ahead
forecasting of monthly streamflow by using a large dataset. Our aim is to compare the results …

The simplest way to forecast geophysical processes, an engineering problem with a widely
recognized challenging character, is the so-called “univariate time series forecasting” that …