River streamflow is closely related to the surface runoff over a river basin. It is a quantity which can be measured with relatively high accuracy. In regional hydrologic models, surface runoff is usually calculated as the residuum of the surface water balance equation and is one of the most uncertain model outputs, especially for mountainous areas. In this paper, we propose a simple method wit...

Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes three-step artificial intelligence model improvement for forecasting. Step 1 uses long short-term memory (LSTM), an on conventional neural network (ANN). 2 performs multi-step ahead while establishing rates change as new approach. 3 further improves through three different kinds optimization algori...

In this paper, an artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) with back-propagation (BP) is proposed to forecast the daily streamflows in a catchment located in a semi-arid region in Morocco. The PSO algorithm has a rapid convergence during the initial stages of a global search, while the BP algorithm can achieve faster convergent speed ...

Stochastic simulation and forecasting of hydroclimatic processes, such as precipitation and streamflow, are vital tools for risk-based management of water resources systems. Stochastic hydrology has a long and rich history in this area. The traditional approaches have been based on mathematical models with assumed or derived structure representing the underlying mechanisms and processes involve...

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for co...