# Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies
## 3 Results and Discussion
### Evaluation of ANFIS Models  
The two main ANFIS architectures were developed to find the effectivity of ANFIS topology for predicting the almond nut yield based on energy inputs. To attain the best result, the following key modifications were made:  
I: type of input MFs (triangular, trapezoidal, bell, Gaussian, and sigmoid);  
II: type of the output MFs (fixed or linear);  
III: number of input and output MFs, the optimization method (hybrid or back-propagation);  
IV: number of epochs. The optimal findings for the first ANFIS model are shown in Table 4. From the results, Gaussian MF, along with hybrid learning methods, yielded the best result. The hybrid learning method combines least-squares (LS) and back propagation (BP) algorithms. LS estimates the parameters associated with output MF and BP tunes the parameters associated with input MF [44]. The epoch number for training the model was selected as 40, since more epochs lead to a very small variation of error. Furthermore, it has been stated that the hybrid optimization method causes better results than the propagation learning algorithm. The total number of parameters in the network is assessed by the number of MFs for input factors. The information from ANFIS for the first model is shown in Table 5. The overall number of training data sets was evaluated to be 119, and the overall number of parameters for ANFIS1-ANFIS7 was 28, representing that the number of MFs for inputs was chosen suitably. The MSE, RMSE, and MAPE for the final ANFIS network were obtained as 0.295, 0.543, and 0.055, respectively. Evaluation among the findings of the three steps exposes that the statistical factors of the second step (including ANFIS 5 and 6) were higher than those of the first step, and subsequently, they were lower than the values of the ANFIS 7. Figure 5: Comparison between measured and predicted values of the almond nut yields for the training, validation, and testing of the optimal ANN algorithm. The best outputs of the second ANFIS topology are demonstrated in Table 6. From the table, the Gaussian combined with linear MFs along with the hybrid learning techniques resulted in the best prediction. The characteristics of the best prediction for the second ANFIS model are illustrated in Table 7. As shown in Figure 4, three input parameters were entered into ANFIS 1, 2, and 4. Therefore, the number of MFs was determined as 2, 2, 2 for ANFIS 1, 2 and 3, 3, 3 for ANFIS 4, while for ANFIS 3, it was selected as 2, 2. Correspondingly, the total number of parameters for ANFIS 1-2 and ANFIS 4 was computed to be 44 and 126, respectively, while for ANFIS 3 the parameter was obtained to be 20. The MSE, RMSE, and MAPE for the ANFIS 4 were determined to be 0.290, 0.538, and 0.048, respectively, which means that the second ANFIS model can estimate output energy with high accuracy. In a case study, [PERSON] et al. [30] employed ANFIS to predict the grain yield of wheat in Iran. Due to eight inputs, they clustered the input vector for ANFIS into two groups and trained two networks. Diesel fuel, fertilizer, and electricity energies were employed as the input variables for ANFIS 1, and human labor, machinery, chemicals, water for irrigation, and seed energies were considered for ANFIS 2. They found the RMSE and R\({}^{2}\) were 0.013 and 0.996 for ANFIS 1, and 0.018 and 0.992 for ANFIS 2, respectively. Finally, they used the predicted values of the two networks as the inputs to the third ANFIS and found that the RMSE and R\({}^{2}\) values for ANFIS 3 were 0.013 and 0.996, respectively. In a study conducted by [PERSON] et al. [23] to model energy consumption in tomato