This paper presents to study the performance of machine learning techniques consisting of Multivariate Adaptive Regression Spline (MARS), Feed Forward Neural Network-Back Propagation (FFNN-BP), and Decision Tree Regression (DTR) for estimating physico-chemical properties groundwater in coastal plain area in Vinh Linh and Gio Linh districts of Quang Tri province of Vietnam. With the amount of 290 groundwater samples collected in two districts, this study has identified three main elements CO2, Ca, CaCO3 for simulation. Quantitative analysis results have shown that these three components are such as CaCO3 with from 0 to 25.8 mg/l, Ca from 0 to 87.55 mg/l and CO2 from 0 to 12 mg/l. In the present examination, groundwater quality index (GQI) values and their representative categories have been referred by the Vietnam Groundwater Standard (QCVN01). Furthermore, the statistical accuracy parameters were used to compare among models. To deploy the FFNN-BP and DTR, different types of transfer and kernel functions were tested, respectively. Determining the results of MARS, FFNN-BP and DTR showed that three models have suitable carrying out for forecasting water quality components. Comparison of outcomes of MARS model with FFNN-BP, DTR models indicated that this model has good performance for forecasting the elements of water quality, its level of accuracy was slightly more than other. To assess the accurate values of the models according to the measurement parameters for training phase illustrated that order models were MARS to give the best result, followed by DTR and finally FFNN-BP, respectively.

The prediction of precipitation is of importance in the Thua Thien Hue Province, which is affected by climate change. Therefore, this paper suggests two models, namely, the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) model, to predict the precipitation in the province. The input data are collected for analysis at three meteorological stations for the period 1980–2018. The two models are compared in this study, and the results showed that the LSTM model was more accurate than the SARIMA model for Hue, Aluoi, and Namdong stations for forecasting precipitation. The best forecast model is for Hue station (= 0.94, = 0.94, = 8.15), the second-best forecast model is for Aluoi station ( = 0.89, = 0.89, = 12.72), and the lowest level forecast is for Namdong station ( = 0.89, = 0.89, = 12.81). The study result may also support stakeholderswho apply these models with future data to mitigate natural disasters in Thua Thien Hue.