algorithm

Solar radiation is very important as one of the important variables in energy balance models and plant growth simulation. Although the measurement of this variable has a relatively long history in Iran, due to the high costs of measuring devices, there is no pyranometer in many existing stations in the country, and there are problems such as its recalibration, water, and dust accumulation that exists on the sensor. Even in meteorological stations that measure radiation, there are days when radiation data is not recorded or unrealistic values outside the expected range are observed due to equipment violations or other problems.This research investigated the performance of nine machine learning algorithms including linear regression (LR), Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (EN), K-Nearest Neighbors (kNN), Decision Tree (DT), Support Vector Regression (SVR), Random Forest (RF), Extra Trees (ET) and Gradient Boosting Machine (GBM) to estimate solar radiation in Yazd synoptic station between 2005 and 2021 with cross-validation method (kfold). The parameters of average temperature, minimum temperature, maximum temperature, sunny hours, relative humidity, and solar radiation are obtained from the National Meteorological Organization on a daily basis and extraterrestrial radiation variables, relative distance from the earth to the sun, solar inclination angle, and maximum sunny hours are calculated with existing relationships and were selected as input for the prediction models. The evaluation criteria for solar radiation estimation were MSE (Mean Square Error), MAPE (Mean Absolute Error), and determination Coefficient (R2).The results showed the coefficient of determination (R2) varies between 0.716 and 0.870 depending on the algorithm used in the training phase. In other words, in terms of the determination coefficient, all the used algorithms showed good results for predicting solar radiation. According to the results of all three criteria, it can be seen that the Support Vector Regression (SVR) algorithm has performed better than other algorithms. After the support vector regression (SVR) algorithm, the linear regression (LR) algorithm was ranked next with the MAPE of 5.04, the MSE of 1.13, and the R2 of 0.867. Also, the elastic pure regression algorithm (EN) with the highest mean absolute value of error (MAPE), the highest mean squared error (MSE), and the lowest coefficient of explanation (R2) ranked last among the nine used algorithms. After the data training phase, using the K-fold cross-validation method, the remaining 20% of the data were tested. As the results show, the MSE changes for nine machine learning algorithms are high and vary from about 3 to 9 Mj/m2/day. Its highest value was observed in the DT algorithm and its lowest value was observed in the support vector algorithm. The average value of the absolute value of the error was also in the range of 0.8 to 2.2 Mj/m2/day, and also the values of the R2 were different in the range of 0.7 to 0.9. In general, and according to the results of all three evaluation criteria, the support vector machine algorithm showed the best results in the data test stage as well as in the training stage.

Assessment and controlling groundwater quality have an important role in planning and developing water resources. Therefore, the use of an efficient method can greatly increase accuracy and reduce costs in this field. In this study, 6 optimization algorithms Consist of Particle swarm optimization (PSO), Genetic algorithm (GA), Imperialist competitive algorithm (ICA), Fireflies algorithm(FA), Cultural algorithm(CA) and covariance matrix adaptation evolution strategy- Evolution strategies (CMA-ES) were used to train and optimize the parameters of the neural-fuzzy inference system model (ANFIS) to model the groundwater quality of Bafgh plain in Yazd province. At first, to select the best combination of input for an estimate of the electrical conductivity (EC), sodium adsorption (SAR) and total hardness (TH), Pearson and Spearman's methods were used to analyze the sensitivity and correlation of other parameters. then qualitative modeling is done with hybrid methods and the performance of the models was measured by correlation coefficients (R2), root mean square error (RMSE), and Nash-Sutcliffe Efficiency (NSE). The results showed that all six combined methods showed a very good performance in modeling groundwater parameters. Also, the ANFIS-FA model was one of the best models in all three modeling parts. So that the value of R2, RMSE and NSE for the test part in TH was, 0.99, 0.41, 0.99, for SAR, 0.98, 1.11,0.95 and for EC 0.99, 305.7 and 0.99. Other methods have also succeeded in modeling and predicting the desired parameters with proper precision. According to the accuracy of the calculations, these methods are suitable alternatives for the prediction of groundwater quality variables.