In the present study, the outlet water temperature from flat plate solar collector using artificial neural networks (ANNs) and support vector regression ((SVR)) was modeled and compared with experimental results. Based on the results, with increasing input parameters of models, the accuracy of the model was increased. According to the results the values of R2, RMSE and MAPE in the (SVR) method for the first model were 0. 97, 3. 25 and 2. 77, respectively. While these values for the second model was 0. 99, 0. 10 and 0. 55, respectively. On the other hand, for the ANN method and for the first model these values were 0. 99 and 0. 02 and 0. 28, respectively. And for the second model were 0. 99 and 0. 01 and 0. 19, respectively. The results showed that the accuracy of artificial neural network model for peridicting the water outlet temperature was better than that of the support vector regression model.

In this article, an approach for fitting a fuzzy linear regression model based on support vectors is presentedwhen the response variable, model parameters and errors are considered as fuzzy numbers.In this method, the objective function is based on the sum of the absolute values ​​of the distances of the hypothetical points to the non-parallel border hyperplanes. The presented model has good robustness to the presence of outlier data. The proposed model has been compared with some other models based on three goodness of fit indices.

Increasing the number of processor cores leads to increasing the density of the computing power processor and also raising the temperature. Temperature management is very important in these processors. Thermal management methods are introduced to reduce the CPU temperature. Reactive and proactive approaches are two sets of these schemes. Unlike the reactive techniques, proactive methods predict the temperature using thermal prediction model before reaching its threshold. In this paper, a hybrid model of several (SVR) models is proposed for predicting temperature. An appropriate dataset is created for training proposed model that includes a high diversity of processor temperature variations. Some features of dataset are measured using temperature sensors and system performance counters. Other features, with historical and control names are calculated with the proposed processes to increase the accuracy of thermal model. Two (SVR) models are used in the proposed thermal model to reduce its operational overhead. The proper features for each (SVR) model are selected by the feature selection algorithm based on mutual information. The proposed model is evaluated for temperature prediction for 2 to 5 time distances. The results show that with a selection of 11 features for thermal prediction model of the next 2 seconds, the mean absolute error is about 0.5oC.

‎The high-dimensional data analysis using classical regression approaches is not applicable, and the consequences may need to be more accurate. This study tried to analyze such data by introducing new and powerful approaches such as support vector regression, functional regression, LASSO and ridge regression. On this subject, by investigating two high-dimensional data sets (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square support vector regression (LS(SVR)) and regression Tree (RT) are used. The LS(SVR) model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also, in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features have been diminished, so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LS(SVR) model has better performance. Finally, the results present these models as suitable models in river flow forecasting.

Background and Objectives: Indiscriminate use of water resources and the occurrence of drought in recent years have caused many problems in the country's water resources. The increasing shortage of water resources and high irrigation costs require developing new irrigation methods for optimal water consumption, which can minimize the amount of water used to produce yields. Evapotranspiration is one of the most important parameters needed to estimate the water balance in any ecosystem. Evapotranspiration is an essential parameter in the hydrological cycle process in natural ecosystems, which links the water and energy balance of the earth's surface with the atmosphere. Reference evapotranspiration (ET0) plays an important role in the availability of water resources and stimulating the hydrological effect of climate change. Accurate estimation of ET0 is necessary for forecasting climate changes, predicting and monitoring droughts, assessing the lack of availability of water resources, assessing crop water needs, and planning irrigation. FAO's Penman-Monteith method is known as a standard reference method for estimating ET0. However, this model and, in general, water balance-based assessment methods require accurate and long-term meteorological data, which are not always and everywhere available. Therefore, alternative methods for predicting ET0 at different temporal and spatial scales should be developed, which are easily applied and require fewer input data without compromising the estimation accuracy. Also, due to the high rate of evapotranspiration in the coastal and central stations of the country, so far, few studies have predicted the ET0 parameter. Therefore, this study was carried out to predict daily reference evapotranspiration in Isfahan and Astara stations.Methodology: The current study is forecasting daily reference evapotranspiration in two stations of Astara and Isfahan using Gaussian Process regression (GPR), support vector regression ((SVR)), M5P tree model, and M5Rules linear regression. For this purpose, the daily meteorological data of the stations including average temperature, minimum temperature, maximum temperature, average relative humidity, minimum relative humidity, maximum relative humidity, wind speed, and sunshine hours during the period of 1990-2021 as inputs to the models was used. Also, to evaluate the effectiveness of the models, the evaluation criteria of determination coefficient (R2), root mean square error (RMSE), Nash-Sutcliffe coefficient (NS), and Wilmott's index of agreement (WI) were used.Findings: The evaluation of the results of different scenarios of the GPR model in Astara station showed that the fifth scenario was recognized as the best scenario of this model due to having a lower error value (RMSE=1.52 mm day-1). For the M5Rules model, the fifth scenario has performed better than the other scenarios of the M5Rules model due to having fewer inputs and similar errors compared to the sixth to eighth scenarios (RMSE=1.42 mm day-1). In the M5P model, the fifth scenario has a higher accuracy than the other scenarios due to having a lower error value (RMSE=1.42 mm day-1). For the (SVR) model, the sixth scenario with the least error (RMSE=1.58 mm day-1) was selected as the best scenario compared to other scenarios of the (SVR) model. For the Isfahan station, for the GPR model, the fifth scenario has performed better than the other scenarios due to having fewer inputs. The comparison of M5Rules model scenarios also showed that the eighth scenario with RMSE=1.85 (mm day-1), had higher accuracy than other scenarios. The seventh scenario of the M5P model has performed better than other scenarios due to its RMSE=1.86 (mm day-1). Finally, the evaluation of (SVR) model scenarios showed that the eighth scenario with RMSE=1.88 (mm day-1) had a better performance than other scenarios. Conclusion: The comparison of the models used to predict daily reference evapotranspiration in Astara station showed that the fifth scenario of M5P and M5Rules models having evaluation criteria of R2=0.76, RMSE=1.42 (mm day-1), NS=0.7 and WI=0.89 had the highest accuracy compared to other models and showed the best performance. Also, the evaluation of the results of the models in Isfahan station showed that the eighth scenario of the M5Rules model, having the evaluation criteria of R2=0.8, RMSE=1.85 (mm day-1), NS=0.8 and WI=0.94 had the best performance compared to other models and the M5Rules model was selected as the best model. Also, the seventh scenario of the M5P model had almost the same performance as the eighth scenario of the M5Rules model and showed a good performance. Therefore, M5P and M5Rules models successfully predicted reference evapotranspiration. One of the limitations of the present study is the lack of access to dew point temperature and solar radiation data. Therefore, the use of these parameters is suggested for further studies.