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Understanding and predicting water evaporation patterns and factors is a critical issue in places in the world where water is a rare commodity, tropical countries which have large surface of water and high temperatures, or in desert countries with very high temperatures almost all over the year such as the state of Kuwait. Understanding different patterns of evaporation rate process is necessary as well as important. Previous literatures mainly focus onto using deterministic approach in understanding such patterns and favoring approximating its behaviors using concrete estimated equations. One of the most promising areas of understanding stochastic patterns from data like this is machine learning. This paper uses real data of different related attributes collected from the environment of the state of Kuwait to build, train, and test an accurate behavioral model created by different machine learning algorithms to do prediction of such process. In this paper, the process of predicting water evaporation rate was formed into two types of problems: classification and prediction. The paper used multilayer perceptron neural network for the prediction purpose. Experiments show very promising and superior results.
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