Groundwater level forecasting using artificial neural networks. A proper design of the architecture of Artificial Neural Network (ANN) models can provide a robust tool in water resources modeling and forecasting. The performance of different neural networks in a groundwater level forecasting is examined in order to identify an optimal ANN architecture that can simulate the decreasing trend of the groundwater level and provide acceptable predictions up to 1. Messara Valley in Crete (Greece) was chosen as the study area as its groundwater resources have being overexploited during the last fifteen years and the groundwater level has been decreasing steadily. Seven different types of network architectures and training algorithms are investigated and compared in terms of model prediction efficiency and accuracy. The different experiment results show that accurate predictions can be achieved with a standard feedforward neural network trained with the Levenberg–Marquardt algorithm providing the best results for up to 1. Groundwater level forecasting using artificial neural networks. Artificial neural networks in hydrology, parts I and II. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Artificial Neural Networks In Hydrology Chicago
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000). I: Preliminary Concepts.' J. Artificial Neural Networks in Hydrology (Water Science and Technology Library) . GOVINDARAJU and ARAMACHANDRA RAO School of Civil. Artificial neural networks in hydrology by the asce task committee a paper review. 124 / JOURNAL OF HYDROLOGIC ENGINEERING / APRIL 2000 ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY. II: HYDROLOGIC APPLICATIONS By the ASCE Task Committee on Application of Arti Artificial Neural Networks In Hydrology Books
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