Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …

Monthly streamflow forecasting is required for short-and long-term water resources
management especially in extreme events such as flood and drought. Therefore, there is …

Accurate runoff forecasting plays an important role in management and utilization of water
resources. This paper investigates the accuracy of hybrid long short-term memory neural …

The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …

Accurate long-term streamflow and flood forecasting have always been an important
research direction in hydrology research. Nowadays, climate change, floods, and other …

Watershed-scale nonpoint source (NPS) pollution models have become important tools to
understand, evaluate, and predict the negative impacts of NPS pollution on water quality …

Eight data-driven models and five data pre-processing methods were summarized; the
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …

Recently, there has been an increased emphasis on employing data-driven models to
forecast streamflow. However, in these data-driven models used for forecasting monthly …

Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature,
traditional and artificial intelligence models have been applied to this task. An attempt to …