Sungwon Kim

TL;DR: Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

TL;DR: In this paper, a generalized regression neural networks model (GRNNM) embedding the GA in order to estimate and calculate the pan evaporation (PE) and the alfalfa reference evapotranspiration (ET r) was developed and evaluated through the training, the testing and the reproduction performances, respectively.

TL;DR: The results showed that the proposed MM-ANN-1 and MGGP-1 models were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations, and will help to the local stakeholders in terms of water resources management.

TL;DR: In this paper, the authors developed and applied the neural networks models to estimate daily pan evaporation (PE) for different climatic zones such as temperate and arid regions, Republic of Korea and Iran.

TL;DR: The proposed SVR-PSO methodology has demonstrates an effective engineering strategy that can be applied in problems of structural and construction engineering prospective, applied to predict shear strength of steel fiber reinforced concrete beam using advanced hybrid artificial intelligence models developed in this study.