Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages. However, TSF has received little attention because a thunderstorm event combination intricate and unique weather scenarios high instability, making it difficult to predict. To close this gap, we proposed two novel hybrid machine learning models through hyb...

in this study, optimized adaptive neuro-fuzzy inference system (oanfis) was employed on a set of daily, weekly, 10-days and monthly data of inflow water into the alavian dam to predict the real-time inflow of the reservoir. sequential and exhaustive search algorithms were used to determine the numbers and time steps of the model inputs and also reducing the prediction’s errors. in sequential se...

precipitation is one of the hydrological processes that play an important role in controlling water resources management. shortage of rain causes some problems such as lack of drinking water. due to the importance of the issue of water shortage, using modern methods to predict hydrological processes will play an important role in planning and management of water resources. therefore, in this st...

Axios River is one of the most important transboundary rivers between the Greek and the neighbour country FYROM in the Balkan area. In this paper, Artificial Neural Networks (ANNs) were used to derive and to develop models for prediction the monthly values of some water quality parameters of the river Axios at a station located at Axioupolis site of Greece near the Greece FYROM borders by using...

This study describes an EOF-based technique for statistical downscaling of high spatial resolution monthly mean precipitation from large scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely-separated locations: the Southeast United States, the Upper Colorado River Basin, China’s Jiangxi Province, and Central Europe. For each location, the EOF-based downs...