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Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Cañar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed.
Using models of organic matter degradation and dissolved oxygen consumption, the concentrations of these compounds are analyzed in two stretches of a river after a discharge of raw sewage. The analyzed river has low drafts and widths, so the velocity is high and the aeration coefficient kr calculated with the Covar method is high, this indicates a rapid recovery of oxygen from the water consumed by the organic matter degradation processes, the river has been instrumented to measure flows and organic matter at various points to calibrate the model. The hydraulic parameters of the river section are analyzed in three control points, in each one sample are taken to analyze oxygen consumption by organic matter and nitrification through laboratory tests to determine and adjust the kinetics of the processes (kd; knit). This kinetics have been used in the development of a water quality model to verify its adjustment, obtaining higher RMSE results than with kinetics from secondary sources. It is observed that the river has an excellent capacity for self-purification due to the high income of dissolved oxygen, with a kr > 9 d-1.
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