Neural Networks is an information processing technique by which the signal transmission system of nerve cells in the brain is designed as an artificial model in order to imitate the excellent information processing performance of the human on a computer. As a general characteristic concerning Neural Networks, it is known that the Neural Network is effective to the information processing when there is some vagueness in the targeted data or when the causal relation between the cause and the result cannot be defined clearly.
　This report is a basic study aimed at the information processing performance of this Neural Network and the best way to use this effectively in the future for various phenomena with difficult elucidation of the causal relations between elements in environments surrounding harbors. This discussion involves the basic operational characteristics of Neural Networks, output characteristics when varying execution parameters, methods of presenting the study data to obtain highly accurate output, effectiveness of use as feedback type network, and the fractal dimension used as the method of deciding optimum forecast period when the time series data is targeted. Finally, the amount of chlorophyl-a was forecast based on results of these investigations from plural water quality data which were actually observed and the potential applications of Neural Network to the real data was examined.