This paper presents to study the performance of machine learning techniques consisting of Multivariate Adaptive Regression Spline(MARS), Multilayer Perceptron (MLP), and Decision Tree Regression (DTR) for estimating physico-chemical properties groundwater in coastal plain area in Vinhlinh and Giolinh districts of Quangtri province of Vietnam. To deploy the MLP and DTR, different types of transfer and kernel functions were tested, respectively. Determining the results of MARS, MLP and DTR showed that three models have suitable carrying out for forecasting water quality components. Comparison of outcomes of MARS model with MLP, DTR models indicates that this model has good performance for forecasting the elements of water quality, its level of accuracy is slightly more than other. To assess the accurate values of the models according to the measurement parameters indicated that order models were MARS, DTR, and MLP, respectively.
This study examines rainfall forecasting for the Perfume (Huong) River basin using the machine learning method. To be precise, statistical measurement indicators are deployed to evaluate the reliability of the actual accumulated data. At the same time, this study applied and compared two popular models of multi-layer perceptron and the k-nearest neighbors (k-NN) with different configurations. The calculated rainfall data are obtained from the Hue, Aluoi, and Namdong hydrological stations, where the rainfall demonstrated a giant impact on the downstream from 1980 to 2018. This study result shows that both models, once fine-tuned properly, enjoyed the performance with standard metrics of R_squared, mean absolute error, Nash–Sutcliffe efficiency, and root-mean-square error. In particular, once Adam stochastic is deployed, the implementation of the MLP model is significantly improving. The promising forecast results encourage us to consider applying these models with future data to help natural disaster non-stop mitigation in the Perfume River basin.
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