Investigation of the Differential Power of Young's Internet Addiction Questionnaire Using the Decision Stump Tree.

Background: Internet addiction is one of the serious consequences of recent advances in the use of social media. Early detection of Internet addiction is essential because of its harms and is necessary for timely and effective treatment. Aim: The aim of this study was to use data mining and an artificial intelligence algorithm to estimate the differential power of each question in the Young Internet Addiction Test and build a decision stump model to predict which item in the questionnaire can be representative of the whole questionnaire. Methods: This is a descriptive study conducted at the University of Tabriz, in which 256 undergraduate students were selected in randomized cluster sampling, and they completed Young's IAT (Internet Addiction Test) questionnaire and some demographic questions. The data were statistically analyzed with SPSS and were divided into two groups, normal and addicted, by using a cut-off point. Also, the data of the subjects was used to model the decision stump tree in WEKA. The clustering item was the normal and addicted specifier. Results: The study shows that Cronbach's alpha of the IAT is 0.88, which shows good internal integration of subjects that are used to develop the model in WEKA (the Waikato Environment for Knowledge Analysis). Data analysis showed that by using the second question of this questionnaire as the root of the decision stump tree model, it is possible to distinguish between Internet addicts and healthy users with 82% accuracy using this model. Conclusion: The study shows innovative ways in which decision stump trees and data mining can help to improve methods used in Clinical Psychotherapy and Human Science. Regarding this, the study showed that early detection of Internet addiction would be possible by using the 2nd question of the IAT. Also, early detection can result in cost-effectiveness for the whole healthcare system.

Trend and abrupt change analysis in water quality of Urmia Lake in comparison with changes in lake water level.

Urmia Lake, as the largest lake in Iran borders, has a special role in the ecosystem of the region. The water level in this lake declines in recent year remarkably, so monitoring the lake water quality is important from an environmental view. In this research, the changes in the qualitative variables of the lake water (including electrical conductivity (EC), pH, total dissolved solids (TDS), and sodium adsorption ratio (SAR)) are compared with the changes in the lake's water level based on the Mann-Kendall nonparametric test. Further, abrupt change points in the time series of quality variables were detected by the Pettitt test. Studies were carried out on samples collected from five different stations during 2005-2015. The results showed that the water level of Urmia Lake had a significant decreasing trend and also, except for TDS, the other investigated quality variables had negative trends during the studied period. It was observed that in general, the values of the Z statistic in the stations located in the eastern part of the lake were more than the stations located in the western part, and also the stations located in the northern parts had a higher trend than those in the south of the bridge.

Estimation of sodium adsorption ratio in a river with kernel-based and decision-tree models.

The control of surface water quality plays an important role in the management of water resources. In this context, the estimation and assessment of sodium adsorption ratio (SAR) are required which is one of the significant water quality parameters in the agricultural production sector. Chemical analysis might not, however, be feasible for a longer period of time in all the country-scale rivers. Therefore in this study, a support vector regression (SVR) model with different kernel functions; K nearest neighbour algorithm; and four decision-tree models, namely, Hoeffding tree, random forest, random tree, and REPTree, were used to estimate the SAR value with minimal parameters in the Aladag River in Turkey. In alternative scenarios, a correlation matrix and sensitivity analysis were used to ascertain the model inputs from among the 15 distinct parameters. All 15 parameters were utilized as model inputs in the first scenario, and only the sodium (Na) parameter was utilized as the model input in the final scenario. The accuracy of the aforesaid models was then assessed making use of correlation coefficient, Nash-Sutcliffe model efficiency coefficient, root mean square error, mean absolute error, and Willmott index of agreement. The results indicate that the SVR model with the poly kernel function provides the best estimates of SAR among the considered models. According to the findings, there is no considerable difference between the results acquired in the first and last scenarios, and one can determine the SAR value while making use of machine learning approaches taking into account only Na parameter.

Estimation of sodium adsorption ratio indicator using data mining methods: a case study in Urmia Lake basin, Iran.

Water quality is a major concern around the world, particularly in dry climates. Usually, assessment of surface water quality is costly and time-consuming. In this situation, a method which could estimate the water quality accurately with the minimum of hydro-chemical parameters would be appealing. In this study, three data mining methods, namely, M5 model tree, support vector machine (SVM), and Gaussian process (GP), were employed to estimate the sodium adsorption ratio (SAR) indicator in the Shahrchay River located in the west of the Urmia Lake basin, Iran. Results from these methods were compared with an artificial neural network (ANN). Different hydro-chemical parameters were assessed and the most effective parameters were selected. Five combinations of the selected parameters were developed as input parameters to the models. The results indicated that the M5 model tree has a superior performance among the data mining methods, where the combination of sodium and electrical conductivity (Na and EC) is used as input parameters, with a coefficient of determination (R2) = 0.987, root mean squared error (RMSE) = 0.017, mean absolute error (MAE) = 0.012, and mean relative error (MRE) = 5.584. Also, a sensitivity analysis was carried out which reported that the SAR is more sensitive to Na, Ca, and EC, respectively. This research highlights that the M5 model tree can be successfully employed for the estimation of SAR. It also indicates that the practical and simple linear equations and optimization performed with the M5 model tree reduce time and cost.

Prediction of Groundwater Level in Ardebil Plain Using Support Vector Regression and M5 Tree Model.

The Ardebil plain, which is located in northwest Iran, has been faced with a recent and severe decline in groundwater level caused by a decrease of precipitation, successive long-term droughts, and overexploitation of groundwater for irrigating the farmlands. Predictions of groundwater levels can help planners to deal with persistent water deficiencies. In this study, the support vector regression (SVR) and M5 decision tree models were used to predict the groundwater level in Ardebil plain. The monthly groundwater level data from 24 piezometers for a 17-year period (1997 to 2013) were used for training and test of models. The model inputs included the groundwater levels of previous months, the volume of entering precipitation into every cell, and the discharge of wells. The model output was the groundwater level in the current month. In order to evaluate the performance of models, the correlation coefficient (R) and the root-mean-square error criteria were used. The results indicated that both SVR and M5 decision tree models performed well for the prediction of groundwater level in the Ardebil plain. However, the results obtained from the M5 decision tree model are more straightforward, more easily applied, and simpler to interpret than those from the SVR. The highest accuracy was obtained using the SVR model to predict the groundwater level from the Ghareh Hasanloo and Khalifeloo piezometers with R = 0.996 and R = 0.983, respectively.