Solar energy is used in many applications such as producing agricultural food, renewable energy, and heating and lighting systems… etc. Nowadays, countries all over the world, especially the developing countries are facing a great challenge which is providing sustainable energy for consumers. Electricity is the most common type of energy that is used by consumers in which oil or nuclear power is used to produce sufficient amount of electricity for the constant increase of the population in the present. However, both oil and nuclear energy negatively affect the global warming; therefore, solar energy is aspired by many countries to decrease the effects of the global warming and produce renewable sources of energy. The aim of this study is to predict the use of solar radiation for solar energy to produce electricity in Duhok city due to the fact that "national electricity" is not enough for the great number of consumers; as a result, people depend on "local or private generators" which mainly depend on oil to produce electricity. Fuzzy logic approach is used to estimate the solar radiation. The four fuzzy systems are created using the available data in Duhok City in 2016. Daily observations for temperature, humidity and wind speed for four seasons are analyzed to estimate the solar radiation. The predicted outputs of fuzzy logic system are compared with the actual solar radiation. In addition, the fuzzy system approach is evaluated using Mean Absolute Percentage Error (MAPE) and Absolute Percentage Error (APE). The outcomes of MAPE and APE are 5. 86%, 1.54%, 2.76% and 1.52 for four seasons (winter, summer, spring and fall), respectively. According to the results, the performance of fuzzy system is reasonably effective in predicting the solar radiation.

The most appropriate method of communicating water quality situation of water bodies is the Water Quality Index (WQI); while user participation and dealing with uncertainty are required for the evaluation of WQI. The aim of WQI is to convert complicated water quality data to information which can be used and understood by users. This index is vital for users to know the gradation of suitable (fresh) water and unsuitable water which might be poisonous and cause serious diseases sometimes. The index might also be used to test the water quality before drilling water wells which are costly and can be really harmful to the environment; accordingly, costs and risks can be reduced a great deal. Lately, the algorithms of artificial intelligence which are suitable for nonlinear prediction and dealing with uncertain domains have been implemented in different fields of water quality estimation. The purpose of this study is to estimate the water quality index using data sets obtained from 22 camps located in six districts in Duhok city for the period March to August 2018. The data sets contain six water quality parameters which are Nitrates (NO3), Sulphate (SO4), Total Hardness (TH), PH, Total ALkalinity (T. AL) and Calcium (Ca). This paper uses the application of Adaptive Neuro Fuzzy Inference System (ANFIS) for modeling the estimation of water quality index. This model is utilized to train, test and check the index. Statistical criteria such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are used to assess the performance of the ANFIS model. Investigations show that for estimation WQI, the RMSE values are 0.0346, 0.2109 and 0.0403 for training, checking and testing stages, respectively. While, the values of MSE are 0.0012, 0.0445 and 0.0016 for training, checking and testing stages, respectively. Based on the results of the criteria, the ANFIS estimation model has the ability to forecast the water quality index for Duhok camps with reasonable accuracy, and it is useful and valuable for the estimation of WQI.

For many years, reading rate as word correct per minute (WCPM) has been investigated by many researchers as an indicator of learners’ level of oral reading speed, accuracy, and comprehension. The aim of the study is to predict the levels of WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), and K- Nearest Neighbor (KNN). The data of this study were collected from 100 Kurdish EFL students in the 2nd-year, English language department, at the University of Duhok in 2021. The outcomes showed that the ensemble classifier (EC) obtained the highest accuracy of testing results with a value of 94%. Also, EC recorded the highest precision, recall, and F1 scores with values of 0.92 for the three performance measures. The Receiver Operating Character curve (ROC curve) also got the highest results than other classification algorithms. Accordingly, it can be concluded that the ensemble classifier is the best and most accurate model for predicting reading rate (accuracy) WCPM.

The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmospheric pressure were used as input parameters in order to obtain the daily average of sunshine duration (SD) as the output. The eight-year data were divided into two categories. The first category covers whole years (annually) and the second category is seasonal. To recognize and assess the influence of different input parameters on sunshine duration, six models of ANN have been evolved. The findings showed that in the annual models, the outcomes of RMSE, MAE and R for the model with input parameters (Month, Cloud Level and Average Temperature) were the best results 1.82, 1.175 and 0.89, respectively. As for the season models, the outcomes of RMSE, MAE and R for the autumn season were the best results 1.450, 1.009 and 0.94, respectively. Accordingly, the performance of the artificial neural network is considerably effective in predicting the sunshine duration.