جستجوی مقالات مرتبط با کلیدواژه « decision trees » در نشریات گروه « آب و خاک »

Considering the importance of fresh water for human life and the vulnerability of groundwater sources to all kinds of pollution and the possibility of transferring pollutants to other surface and groundwater sources, as well as the location of Iran in the arid and semi-arid belt, protecting this valuable and rare element is imperative and its continuous monitoring must be one of the priorities of water resources managers. Therefore, in the current research, nitrate pollution in the eastern plains of Mazandaran province was discussed and relevant issues were investigated, and an efficient and optimal model for predicting nitrate concentration was presented. In this research, three machine learning models including decision tree, logistic regression and artificial neural network were compared. The physical and chemical data measured during the years 1985 to 2020 were used and entered as the input variables of the models. The variables include temperature, water level, pH, EC, HCO3-, CL-, SO24-, Na+, K+, Mg2+, Ca2+, TH and TDS; The amount of nitrate contamination of the groundwater, was predicted by dividing 70% of the dataset as training and 30% as testing data. The R2, RMSE, NSE and PBIAS indexes were applied for model evaluation. The results indicated that the Decision Tree model had the best performance with a large difference compared to the other two models (R2 = 0.957 and RMSE = 0.297, NSE = 0.95 and testing acc = 0.907). After That, logistic regression and artificial neural network had much weaker performances than the lead model. It is suggested to conduct another research with other machine learning models by changing the input variables and add some extra ones such as land-use and compare the results with the current research.

Transmissivity is an important factor in identifying the characteristics of aquifers, so, estimating its value and distribution by modeling is necessary for aquifer management. Estimation of this parameter using field experiments such as pumping test is costly and time-consuming. For suitable management of Malikan plain, as one of active agricultureal areas in north-west of the country, understanding the hydrogeological parameters, such as transmissivity is required. In this study the random forest (RF) algorithm, which is a learning method based on ensemble of decision trees, is proposed for predicting transmissivity that has not been used in this field, yet. The RF technique has advantages over other methods due to having high prediction accuracy, ability to learn nonlinear relationships, non-parametric natural and ability to determine the important variables in the prediction process. Increasing the number of trees decrease the error, so 500 trees were selected to reap high efficiency of the model. The model results were evaluated by OOB error estimating method and in addition, to reduce the dimensions, increase the accuracy and better interpretation of the model process, the FS method was used. The most important variables in the prediction were also identified by the FS method. Based on the results of RF modeling with AUC=0.96 and MSE=0.036, electrical conductivity, aquifer media and hydraulic gradient variables were the most important parameters in predicting transmissivity, respectively. Also the accuracy of RF model and determining the important parameters in transmissivity prediction showed the advantages of this model over other models in prediction issue.