جستجوی مقالات مرتبط با کلیدواژه « مدل هیبریدی » در نشریات گروه « کشاورزی »

Predicting river flow is one of the most crucial aspects in water resources management. Improving forecasting methods can lead to a reduction in damages caused by hydrological phenomena. Studies indicate that artificial neural network models provide better predictions for river flow compared to physical and conceptual models. However, since these models may not offer reliable performance in estimating unstable data, using preprocessing techniques is necessary to enhance the accuracy and performance of artificial neural networks in estimating hydrological time series with nonlinear relationships. One of these methods is wavelet transformation, which utilizes signal processing techniques.

In this study, to evaluate the efficiency of discrete and continuous wavelet types in the Wavelet-Artificial Neural Network (WANN) hybrid model for monthly flow prediction, a case study was conducted on the Kardeh Dam watershed in the northeast of Iran, serving as a water source for part of Mashhad city and irrigation downstream agricultural lands. Monthly streamflow estimates for the upstream sub-basin of the Kardeh Dam were obtained from the meteorological and hydrometric stations' monthly statistics over a 30-year period (1991-2020). The WANN model is a hybrid time series model where the output of the wavelet transform serves as a data preprocessing method entering an artificial neural network as the predictive model. The combination of wavelet analysis and artificial neural network implies using wavelet capabilities for feature extraction, followed by the neural network to learn patterns and predict data, potentially enhancing the models' performance by leveraging both methods. The 4-fold cross-validation method was employed for the artificial neural network model validation, where the model underwent validation and accuracy assessment four times, each time using 75% of the data for training and the remaining 25% for model validation. The final results were presented by averaging the validation and accuracy results obtained from each of the four model runs. To evaluate and compare the performance of the models used in this study, three evaluation indices, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Pearson correlation coefficient (R), were employed.

The analysis of meteorological and hydrometric data in this study revealed that monthly streamflow in two time steps, T-1 and T-2, were the most effective predictive variables. Each of the two runoff variables of the previous month (Qt-1) and the previous two months (Qt-2) were analyzed by each of the Haar and Fejer-Korovkin2 discrete wavelet transforms and the two continuous Symlet3 and Daubechies2 wavelets at three levels. The results of each level of decomposition was given as input to the ANN model. The presented results at each decomposition level indicated that hybrid models could accurately predict lower flows compared to the single ANN model, and the estimation of maximum values also significantly improved in the hybrid models. Among the wavelets used, Haar wavelets exhibited the weakest performance, and the less commonly employed Kf2 wavelet showed a moderate performance. Since the Haar and Fk2 wavelets, with their discrete structure, did not perform well in decomposing continuous monthly streamflow data, continuous wavelet models outperformed discrete wavelet models. The hybrid models, combining wavelet analysis and artificial neural networks, demonstrated up to an 11% improvement over the performance of the single neural network model.

Streamflow is a crucial element in the hydrological cycle, and predicting it is vital for purposes such as flood prediction and providing water for consumption. The objective of this research was to evaluate the performance of different types of discrete and continuous wavelet models at various decomposition levels in enhancing the efficiency of artificial neural network (ANN) models for streamflow prediction. Since climate and watershed characteristics can influence the nature of data fluctuations and, consequently, the results of the wavelet model decomposition, choosing an appropriate wavelet model is essential for obtaining the best results. Considering the existing variations in the results of different studies regarding the selection of the best wavelet type, it is suggested to use both continuous and discrete wavelet types in modeling to achieve the best predictions and select the optimal results. Given that a lower number of input variables in neural network models lead to higher accuracy in modeling results, it is recommended to perform decomposition at a two-level depth to reduce input components to the neural network model, thereby reducing the model execution time.

Dez dam has an essential role in controlling the floods and water supply in Khouzestan. Accurate and reliable prediction of the flow input to the reservoir is crucial to make decisions about the exploitation and management of this reservoir. In this research, a novel WAVELET-ARIMA-NARX model was developed to predict the monthly inflow to Dez dam reservoir. The WAVELET method was used to break down the time series into sub-series and better analyzing, the ARIMA model was used to model the linear component of the analyzed series, and the NARX model used to model the error resulting from WAVELET-ARIMA model. The NDVI index was also used to measure the accuracy change of the WAVELET-ARIMA-NARX hybrid model. The results showed that the prediction performance of the WAVELET-ARIMA-NARX hybrid model has improved significantly compared to the ARIMA model. In such a way according to the RMSE criterion, the prediction accuracy in the verification and training stages has decreased by 74% and 82%, compared to the ARIMA and WAVELET-ARIMA model, respectively. The NDVI parameter with average temperature and rainfall of the basin as an input of the NARX model has increased the accuracy of the model. In the model with 10 neurons in the hidden layer, compared to the two-parameter model of rainfall and NDVI with 15 neurons, in evaluation section, the MAE values have decreased from 27.2 to 18.5 and the RMSE from 0.45 to 0.26. These values indicate the importance and impact of simultaneous consideration of three parameters on forecasting accuracy.

In recent years, the fluctuation in agricultural commodity prices in Iran is increased and thus, accurate forecasting of price change is necessary. In this article, a flexible combined method in modeling monthly prices of beef, lamb and chicken from April 2001 to March 2021, was proposed. In this new method, three different approaches namely simple averaging, discounted and shrinkage methods were effectively used to combine the forecasting outputs of three hybrid methods (MLPANN-GA, MLPANN-PSO and MLPANN-ICA) together. In implementation stage of hybrid methods, based on test and error method, the optimal MLPANN structure was found with 3/2/4–6–1 architectures and the controlling parameters are carefully assigned. The results obtained from three hybrid methods indicate that, based on the RMSE statistical index, the MLPANN-ICA method performs the best when forecasting prices for beef, lamb, and chicken. The outputs of three combination approaches show that the shrinkage method, with a parameter value of K=0.25, achieves the highest prediction accuracy when forecasting prices for these three meats. In summary, the proposed method outperforms the other three hybrid methods overall.

Saffron, as the most expensive agricultural and medicinal product in the world, has a special place in buying and selling related to the Iranian agricultural commodity bourse. The saffron commodity bourse, like any other market, has always faced limitations and structural problems, and most of these problems are related to its price fluctuations. Price forecasting using appropriate models can be a great help in reducing the price risk of futures market of saffron. The main question is that in in confronting with various price forecasting methods, which methods should be chosen to forecast the future price of saffron? The purpose of this study is to design the most appropriate hybrid model for forecasting the future price of Negin saffron in the agricultural commodity bourse, which consists of a set of nonlinear models of genetic algorithm, deep neural network, random forest, support vector machine and Monte Carlo method. In this hybrid model, genetic algorithm is used to determine the optimal lag of price time series, deep neural network, random forest model and support vector machine are used to forecast the price time series, and Monte Carlo method is used to simulate the most probable price probability. The results of this study showed that the forecasting accuracy of the hybrid model of "Genetic Algorithm-Deep Neural Network-Monte Carlo" is higher than the two models of "Genetic Algorithm-Random Forest-Monte Carlo" and "Genetic Algorithm-Support Vector Machine -Monte Carlo". Therefore, using a deep neural network and calculating the most probable price probability by the Monte Carlo method, provide the most accurate saffron price prediction with a high degree of reliability and minimal risk. Thus, it is suggested that that the management of the commodity bourse, stock market participants and researchers can use the advantages of this proposed model in forecasting the price of agricultural products.

Water is one of the most essential elements in nature that forms the basis of human life and contributes to the economic growth and development of societies. Healthy water is closely related to environmental health and activities. The life of all animals on Earth depends on water and oxygen. In addition, adequate dissolved oxygen (DO) is essential for the survival of aquatic animals. Therefore, in this study, to simulate the dissolved oxygen of the Cumberland River in the United States from the combined artificial neural network (ANN) model with wavelet and meta-heuristic algorithms of gray wolf (GWO) and bat (BA) on a monthly time scale during the statistical period. Used 2020-2010. The criteria of correlation coefficient (R2), squared mean square error (RMSE), absolute mean error (MAE) and Nash-Sutcliffe productivity coefficient (NSE) were used to evaluate and compare the performance of the models. The results showed that all three hybrid models have better results in hybrid models than the other designated models. Also, according to the evaluation criteria, it was found that among the models used in the simulation of dissolved oxygen in river water, the model of artificial neural network-wavelet with coefficient of determination (R2 = 0.958), the root mean square error (RMSE = 0.651), The mean absolute value of error (MAE = 0.334) and Nash Sutcliffe coefficient (NS = 0.962) in the validation stage showed better performance than other models.

The aim of this study is modeling and predicting the monthly wind speed of Dezful. Therefore, SARIMA model, threshold nonlinear time series model (SETAR), SARIMA-SETAR hybrid model, and also artificial neural networks were used. In addition, the PMI algorithm was used for selecting the effective input variables in predicting wind speed for the neural network model. Using the Hempel criterion and the AIC information criterion monthly relative humidity in the previous two months RH (t-2), monthly in the previous month evaporation (t-1), the average monthly temperature in the previous three months Tave (t-3) and maximum monthly temperature in the previous month Tmax (t) as effective input variables for modeling and predicting monthly wind speed were identifie .TO validate the SARIMA and SETAR models and the fitted SARIMA-SETAR hybrid model, the functions of autocorrelation, partial autocorrelation and the residual independence test of the model (Ljung-Box) were used. The superiority models ware determined based on the minimum numerical value of Schwartz and AIC statistics. For modeling and predicting wind speed with neural network, linear function was used for input and output layer and different stimulus functions with different training algorithms were used for the hidden layer.The neural network model with topology (1-3-5) with sigmoid tangent stimulus function and Levenberg-Marquardt training algorithm was found to have better performance in predicting the monthly wind speed of Dezful synoptic station compared to SARIMA linear and SETAR nonlinear models. The results of the models showed that the SARIMA-SETAR hybrid model (2, 2, 3) had better performance compared to other models. It has acceptable accuracy in predicting the monthly wind speed of the Dezful synoptic, with a coefficient of determination of 0.91 and a root of mean squares 72.

Urban water distribution networks consider as one of the essential infrastructural facilities and equipment in urban areas. The pipes are one of the primary and essential components of a water distribution network break during operation due to various factors. So, developing models for pipes failure rate prediction can be one of the most crucial tools for managers and stakeholders to the optimal operation of the water distribution network. In the last decade, various studies have performed to predict the failure rate of water distribution pipes using statistical and soft models - each of which has strengths and weaknesses. This study aims to present a new approach based on the development of a hybrid prediction model, considering the capabilities of soft and statistical models, to more accurately predict the water distribution network pipes failure rate compared to statistical and soft models used in previous research.

In order to achieve the study goals, 4-year (2015-2018) time duration statistics of Gorgan water distribution network characteristics including diameter, length, age, depth of installation, and the number of pipe failures used to predict future pipes failure rates. To modeling the pipe failure rate of the investigated network, five different models, including three statistical models (linear regression, generalized linear regression, support vector regression) and two soft models (feed-forward neural network, and radial basis function neural network) has studied. Optimal parameters of the models were selected based on appropriate statistical error indicators, including correlation coefficient (R), Mean Square Error (MSE), and Correlation Mean square error Ratio (CMR) for the training and testing data. In order to select the best model from different models to predict the failure rate of network pipes, the values of R and MSE indicators of the above models were calculated in the validation stage and compared with each other. Finally, to predict pipes failure rate more accurately, a new approach is developed based on the hybrid prediction model in which the predicted values of pipe failure rates by statistical and soft computing models considered as independent variables of the best model inputs and the observed values of failure rates as dependent variables of the best model outputs.

Comparing the values of R and MSE indicators of each statistical and soft computing model used in this study in the validation phase show that these models cannot predict the pipes' failure rate with reasonable accuracy. Feed forward neural network model with the highest R = 0.69 and the lowest MSE = 0.062 values has the best estimates. Using the new approach developed based on hybrid soft and statistical models, the R index is equal to 0.96, and the MSE index is equal to 0.046.

A significant increase in the R index (39%) and decrease in the MSE index (25%) through using the proposed hybrid approach compared to the feed-forward neural network model demonstrates that using the new approach provides perfect accuracy prediction of the pipes failure rate of the water distribution network.

In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were   then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station.

Operators usually follow the rule curves for operation of a reservoirs system in actual conditions. Rule curve indicates desired release or target storage volume for a reservoir in the specified period of year. In the present study, combination of a simple genetic algorithm with the simulation model (ARSP) was used for multiple reservoir systems to find optimum rule curves. In this hybrid model, a linear programming was used for water allocation per single period. In addition, the genetic algorithm through a nonlinear programming was utilized for searching the rule curves. Capability of the developed model was evaluated by defining and analyzing of the three dam water resources systems in Zohre River. Calculations showed that the above combination improved the limitation modeling greatly, increased optimization convergence rate and made considerable flexibility for total process of planning and management in complex systems.