جستجوی مقالات مرتبط با کلیدواژه "ann" در نشریات گروه "اقتصاد کشاورزی"

In today's world, water supply and demand management plays a pivotal role in organizing and planning the drinking water supply of urban and rural residents as well as the water supply of farmers and industrialists, especially in the current situation which all countries are facing the consequences of climate change. Therefore, in this study, the water demand of the agricultural sector of the Caspian littoral provinces was predicted by Markov-Switching method and compared with the Artificial Neural Network model using seasonal data for the period 2001: 1 to 2018: 4. Comparing the efficiency of water demand models estimated by Markov-Switching and ANN methods using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) Showed that Markov-Switching approach was more efficient than water ANN models for predicting water demand. In addition, the forecast of agricultural water demand for both seasonal and annual periods was made for the periods of 2019: 1 to 2023: 4 and 2019 to 2023, respectively.

The purpose of this study was to investigate the effect of real exchange rate uncertainty on Iran's agricultural imports for the period of 1977-1389 and predict the level of Iranian agricultural imports by 1404 using GARCH, VAR, VECM and ANN methods. For this purpose, a generalized autoregressive conditional heterogeneity variance model was used to index the real exchange rate uncertainty, using autoregressive patterns and vector error correction for estimating co-integration and short-term and long-term coherent relationships and finally for prediction of artificial neural network method. The results showed that there is an indirect relationship between the real exchange rate fluctuations and consumption pattern of the society on agricultural imports and the direct relation between oil revenue variable and the absorption variable on agricultural imports. Then, comparing the efficiency of self-regression models and Vector Error Correction Model and artificial neural network, a neural network designed to predict the import of Iranian agricultural sector for the period 1396 to 1404 was used under a scenario and exogenous predictions were .....