Prediction the amount of nitrogen dioxide pollution in Tehran city using the fuzzy-neural inference system adapted to the crow learning algorithm.

Today, air pollution due to continuous urbanization has become a global issue in both social and environmental fields, researches have been conducted in this field, Lim et al. in the capital region of Korea through regression modeling. The results indicate a relatively high concentration of NO2 in winter in the present and future forecasts, which is caused by the high use of fossil fuels in steam boilers and showed climate changes [1]. In 2021, Shams et al. evaluated the accuracy of multi-linear regression and multi-layer perceptron neural networks in predicting the concentration of NO2 in the air of metropolises. The results show that the multi-layer perceptron neural network had a more accurate prediction than the multi-linear regression [2].2- An overview of algorithms2-1- Crow's learning algorithmIn this algorithm, crows are trained based on two more optimal solutions which are parents. Another learning is the learning of each crow from its brothers and sisters, and the behavior of crows to hunt worms that are inside the tree trunk is used for modeling. In Crow's algorithm, parents X1, X2 reward their behaviors according to the following matrix. (1) F=2-2- adaptive neural fuzzy inference system ANFIS structure has a good capability in training, construction and classification. Its learning rule is based on the error backpropagation algorithm by minimizing the mean squared error between the network output and the real output. [3]. Figure 1- simple diagram of ANFIS [3] 2-3- Basal-radial neural networkRadial-based neural network is used for non-parametric estimation of multidimensional functions from a limited set of training information. In this network, the hidden layer plays an important role in converting non-linear patterns into linear separable patterns. which is in the form of relation (3):(3) "f" ("x")"=" ∑_"i=1" ^"p" ▒〖"w" _"i" "φ(" 〖"Xc" 〗_"i" "-x)"3- Steps of the proposed

All steps of the proposed method include pre-processing (cleaning, normalization and feature selection) and post-processing (proposed method).In this article, the data of Tehran meteorological station is used, which includes 1000 data samples with 23 features.Then, the fuzzy-neural adaptive inference system is used to predict the amount of nitrogen dioxide pollution. Crow learning algorithm is used to train this system. Figure 3- The structure of ANFIS neural-fuzzy inference systemTo select the parents based on the competence of the population members, the two crows that have the most competence are considered as parents.Figure 4- The learning phase and the new position of the crow after the learning phaseThen, in the evaluation stage, the objective function is called and the mean square error is calculated. Finally, the termination conditions of the iteration are checked based on the lower mean square error. 4- Simulation results 4-1- Prediction results with the proposed method The parameters of the population size of crows are 50 and the maximum number of repetitions is 500, the type of fuzzy inference system is Sogno type and Gaussian input membership functions are considered. Table 1- Types of errors in the proposed method in predicting NO2.Figure 5- Error histogram for training and testing data in the proposed method.Figure 6- Target outputs and outputs of the proposed method for training data.adial-basal neural network.Figure 10 - Target outputs and radial-basis neural network for training data.Figure 11 - Target outputs and radial-basis neural network for the entire test data.Figure 12 - Target outputs and base-radial neural network outputs for the whole data

This article is based on predicting the amount of nitrogen dioxide pollution using machine learning methods. According to tables (1) and (2), the fuzzy-adaptive neural inference system trained with the crow learning algorithm and the radial basis neural network performed the prediction with mean square error of 0.0081 and 0.0101, respectively. Therefore, the best performance belongs to the adaptive neuro-fuzzy inference system trained with the crow learning algorithm.