Examinando por Autor "Naranjo Polania, Diego Fernando"
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- ÍtemPronostico de la precipitación para la zona de influencia de la estación agroclimática Yariguies,utilizando técnicas de Machine Learning(Fundación Universitaria Los Libertadores. Sede Bogotá., ) Naranjo Polania, Diego Fernando; González Veloza, José John FredyPredicting precipitation is ideal because it helps in the planning of agricultural activity and human, in the agronomic activity it could be determined if the hydric requirements of a crop will be presented and thus not lose a harvest, or know how many millimeters needs to achieve to keep the crop hydrated, on the other hand, as a society we Interesting, because it is possible to determine when strong precipitations end or torrential that lead to a flood or landslides that put in danger life. For the statistical analysis, data from the agroclimatic station were taken. Yariguies located in the municipality of Barrancabermeja, department of Santander, country Colombia, the time series is between 07/01/1967 to 09/30/2009, the unit of the precipitation variable is millimeters (mm), in total there were 19266 data, of which 15,412 (80%) were used for training and 3,854 (20%) to test the model, the elaborated models were Holt Winters, Decision Trees and a Sequential Neural Network (GRU), the metrics used were the MAE, MSE and RMSE for the models highlighting the GRU neural network with 0.05, 0.01 and 0.1 mm respectively, however heavy (20-70 mm), intense (70-150 mm) and torrential (>150 mm) rains are not observed in the figure because the error is higher than expected, with the decision tree predict predict heavy, intense and torrential rain but the model fit is not appropriate; despite the fact that the prediction made by the model tends to have a behavior similar to real data, possibly because precipitation data is not linear in nature since quantity, frequency and intensity are three characteristics of the rainfall time series and the values vary by location, day, month and year according to Mohini P., Vipul K., & Harshadkumar B., (2015) and an imbalance in the data because 75% of the database corresponds to rainfall less than 5.3 mm, and 50% to rainfall less than 0.2 mm