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This study explores the river-flow-induced impacts on the performance of machine learning models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay, Pacific Ocean. For this purpose, hourly recorded water quality parameters of salinity, temperature and turbidity as well as the flow data of the Wailuku River were used. Several machine learning models including artificial neural network, extreme learning machine and support vector regression have been employed to investigate the river-flow-induced impact on the water quality parameters from the current time up to 2 h ahead. Following the input structure of the machine learning models, two separate models based on including and excluding the river flow were developed for each variable to quantify the importance of the flow discharge on the accuracy of the forecasting models. The performance of different machine learning models was found to be close to each other and showing similar pattern considering accuracy and uncertainty of the forecasts. The results revealed that flow discharge influenced the water salinity and turbidity of the bay in which the models including the river flow as input variables had better performance compared with those excluding the flow time series. Among the water quality parameters investigated in this research, river flow made the most and least improvement on the efficiency of the models applied for forecasting of turbidity and water temperature, respectively. Overall, it was observed that water quality parameters can be properly forecasted up to several hours ahead providing a potentially valuable tool for environmental management and monitoring in coastal areas.

Abstract.Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.

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