جستجوی مقالات مرتبط با کلیدواژه "موجک" در نشریات گروه "آب و خاک"

In the current research, using artificial intelligence, the productivity of wheat in the city of Sari as the study area has been investigated in terms of other parameters involved in production. The changes in the morphological and physiological characteristics of the four studied wheat cultivars under different tillage treatments and different moisture levels were noticeable and ponderable, so that in most cases the effects of each of the treatments used and also their mutual effects on the evaluated characteristic at different levels 1 and 5 The error percentage was significant. Biological performance as one of the most important factors considered for Niknejad, Chamran, Alvand and Sardari cultivars under conventional tillage and irrigation conditions equal to 16.25, 18.5, 16.5 and 17.2 tons per hectare respectively and Under the condition of no tillage with severe rain stress, 8.3, 8.2, 8.6 and 9.6 tons per hectare were obtained, respectively. The process of changes in performance characteristics for dry Sardari and Alvand cultivars was much less than the changes observed for the two Niknejad and Chamran blue cultivars, and in general, conventional tillage conditions and minimum moisture stress will be necessary to achieve acceptable production for different cultivars, and a significant reduction in Expected performance characteristics do not occur. The effect of cultivated cultivars was more effective on the characteristics of weight of 1000 seeds and height, so that in general this factor can equalize the biological performance and overall production of the plant through other factors such as changes in the density of wheat cultivation, but some characteristics such as being under stress The specific varieties in question, such as Sardari and Alvand varieties, showed more resistance and their performance under stress conditions showed less changes than Chamran and Niknejad varieties in terms of biological production or other investigated characteristics.

Investigating the effect of agricultural management methods on the amount of nitrate and urea in the soil has special importance. The purpose of this study is to model and investigate the interrelation of hydraulic, reactivity, and solute absorption variables of different soil depths collected in the year of (2020-2021) from the pilot rice farm located in the Sari Agricultural Sciences and Natural Resources University, Mazandaran province, with an area of 0.6 hectares. In this study, the residual moisture of the soil (θr), nitrification rate (kn), and urea hydrolysis rate (kh) variables were modeled based on four, four, and two defined scenarios, respectively, using Wavelet-Artificial Neural Network (WANN), Wavelet- Artificial Neural-Fuzzy Inference System (WANFIS), and Wavelet-Gene Expression Programming (WGEP) models. The results showed that the performance improvement percentage of WGEP models compared to WANFIS and WANFIS model compared to WANN considering the RMSE evaluation index were obtained (16.96, 41.87), (85.72, 1.00), and (20.37, 3.27) for three variables of θr, kn, and kh, respectively. the volumetric residual moisture in the soil, and the urea hydrolysis rate variable is also highly dependent on the residual moisture in the soil. Also, the results showed that the hydraulic variables, reactivity and absorption of soil solutes can be affected by the climatic conditions of the region. Therefore, providing intelligent applicable models to estimate nitrate and urea variables in soil can help managers and farmers in proper management of water and soil resources and optimal use of nitrogen fertilizer with less time and cost.

Drought is one of the natural hazards that have random and nonlinear behavior due to its various climatic parameters. SPI index is the most common index extracted from rainfall that has been used in modeling drought by various researchers.

The use of computational intelligence methods to model drought in recent years has been much considered by researchers in the field of water resources. In this research, SVR and GPR algorithms individually and also the combination of these algorithms with wavelet algorithms have been modeled and predicted by SPI index, and the purpose was to evaluate the improvement of computational intelligence algorithms in combination with wavelet. In this research, the time series data of 10 synoptic stations in Iran in the period 1961 to 2017 have been used on a monthly basis for modeling the drought as the input of the studied algorithms.

The results of this study showed that the use of the wavelet method in combination with SVR and GPR computational intelligence algorithms improved the results in all time scales. Also, the modeling improvement is due to the use of wavelet in combination with the SVR model with an average RMSE difference of -0.1540 and R2 difference of 0.1491 and the GPR model with an average RMSE difference of -0.1554 and R2 difference of 0.1530 Compared to the single SVR and GPR models showed that the GPR model in general (all time scales and all stations) had a better improvement in the hybrid model than the single model.

Evaporation as one of the natural parameters has always been considered by researchers. In this study, the monthly evaporation variable was modeled in two different climates of Iran using artificial neural network, adaptive fuzzy-neural inference system and gene expression programming methods and combining these methods with wavelet theory. For this purpose, meteorological data of precipitation, relative humidity, average temperature, maximum temperature, minimum temperature and wind speed were used during the statistical period of 1384-1397 related to the two catchments of Urmia Lake and Gavkhouni. In this study, the seasonal effect and data noise reduction were applied. The accuracy of the studied methods was evaluated based on statistical correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and nash-sutcliffe efficiency (NSE). The results show that in two different climates, the wavelet-hybrid gene expression programming and the single artificial neural network have the highest and weakest performance, respectively, among other data mining models used in this study. The hybrid wavelet-gene expression programming model with RMSE value of 20.870 and 156.884 had higher performance for Tazehkand station in Urmia Lake catchment area and Kuhpayeh catchment in Gavkhouni catchment area, respectively. Also, the results showed that the effect of seasonal factor utilization and data noise reduction in model performance improvement is significant. Based on the results of the models performance Urmia Lake catchment area with Dsa climate has been better. However, hybrid data mining methods can be introduced as a good alternative to the old methods.

Since a considerable part of the available water is wasted due to evapotranspiration, a precise estimation of it in short-term and long-term periods is of great importance. In this paper, the capability of the M5 model tree and random forest (RF), as the artificial intelligence approaches, and in combination with the wavelet preprocessing, investigated to estimate the potential daily and weekly evapotranspiration in the synoptic station of Babolsar. Given the time series structure of the input data, the two functions of Coiflet mother wavelet and Daubechies 6 wavelet in the decomposition levels of 3 to 8 were chosen. The four indices of correlation coefficient (R), index of agreement (Ia), Nash-Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE) used to evaluate the presented models. The obtained results indicated that although all individual models have desirable efficiency in modeling the evapotranspiration, the use of wavelet preprocessing enhances the performance of individual models in all cases while allows the simpler input scenarios to provide more desirable results. For instance, in the third input scenario (wind speed, maximum temperature, relative humidity, and dew point), the use of Daubechies 6 wavelet in the decomposition level of 5 increased the correlation coefficient of the daily model from 0.908 to 0.928 while reduced the RMSE from 0.833 mm/day to 0.722 mm/day. Similarly, the use of Coiflet-4 mother wavelet in the decomposition level of 5 raised the correlation coefficient of the weekly model from 0.948 to 0.961 while lowered the RMSE from 4.55 mm/week to 4 mm/week. Therefore, in the present study, the efficiency of both individual and hybrid approaches in estimating the evapotranspiration of daily and weekly periods is satisfactory. However, if the hybrid approaches employed, even the use of simpler and more accessible meteorological parameters will provide satisfactory results.

Resilience is a criterion for measuring the strength and stability of a system and its ability to absorb changes and distribute them, while at the same time maintain relationships between system variables. The ability of societies to live and develop with dynamic environments is known as ecological resilience. When the accessibility of a vital resource (such as water) varies between overabundance and extreme scarcity due to natural or man-made phenomena, management should be flexible and the authorities adapted with maintaining legitimacy. So, the prediction of ecological resilience in water resources such as rivers is an important issue that needs to be considered for better management of land-use systems and water supplies. For this purpose physical and chemical parameters in rivers should be monitored to predict and understand the behavior, flexibility and interaction between living-beings of rivers. The ecological importance and reorganization features of algae, as well as being directly influenced by chemical and physical parameters make them eligible to be considered as indicators of nutrient pollution and to be the endpoints for numeric nutrient criteria developed for water quality management aims. But, most environmental models do not address water quality in relation to river biology over time and offer little prediction for future periods. Time series modeling and forecasting have importance in various applied studies, such as resilience in which resistance to chronic stress and time series are to be significant. In spite of various studies on intelligent modeling in the field of water management, no study has yet investigated the environmental resilience of the river in Iran, using the time series and artificial intelligence models. The resilience indicators examined for rivers have included four criteria: the biology, impact of pollutants, climate change, and time. The mentioned criteria were investigated in accordance with the following factors: Diatom algae, chemical parameters, discharge variations, and 10-year time series. The input data for modeling relations in the river ecosystem for Diatom were based on the factors influencing the physical and chemical parameters of these algae (EPA2017), and also on the basis of statistical methods of their correlation coefficients. Resilience index in the current study was determined based on Diatoms population with regard to Diatomic-Trophic index. In this respect, this study proposes a gene expression programming (GEP), hybrid wavelet-gene expression programming (WGEP), support vector machine (SVM) and wavelet support vector machine (WSVM) for prediction of monthly variations in Lavarak Station’s water quality that affect bio indicators. The 10-years (2002-2012) monthly data used in this study were measured from Aliabad River located in Tehran, Iran. First, the measured discharge (Q) and other quality parameters that affect the bio indicators data sets were initially decomposed into several sub-series, using discrete wavelet transform (DWT). Then, this new sub-series was imposed on the (GEP) and (SVM) models as input patterns to predict monthly bio indicator one month ahead. The results of the new proposed WGEP and WSVM models were compared with SVM and GEP models. The performance of this model was evaluated using Nash-Sutcliffe efficiency (NS), root mean square error (RMSE), and mean absolute error (MAE). A comparison between the four models showed the superiority of the hybrid models over the classic models. The achieved results even pointed to the superiority of a single SVM model over the GEP model. With regard to the studies conducted to determine the bio - resiliency index, the abundance of Diatom algae in the river within the standard of resilience the WSVM hybrid model was better while the WGEP was the second best. But due to the modeling process and the results, the WGEP model was used to determine the formula and the effect of each parameter was defined in the scenario. This model can also be effective in expressing changes in one or more independent parameter. The results of this study indicated that considering the capacity and the ability of AI models to deal with the nonlinear nature and dynamics of hydrological processes, the ability of wavelet analysis to extract certain periods of a time series is potentially more to gain reasonable prediction in different environmental processes and planning for them.

Forecasting of river discharge is a important aspect of efficient water resources planning and management. In this study, time series pre and post-processing methods along with support vector machine (SVM) and Gaussian process regression (GPR) kernel based approaches were used to estimate flow discharge of two natural river in the United States with two consecutive hydrometric stations. The first river contained about 2 years of data and in the second river 4 years of daily discharge data was used. Different models were defined based on hydraulic characteristics and the capability of integrated pre and post-processing methods in two states of inter-station and between-stations was investigated. For data pre-processing, the Discrete Wavelet Transform (DWT) method was first used. Then, the high-frequency sub-series were selected and re-decomposed using the Ensemble Empirical Mode Decomposition (EEMD). Finally, sub-series with higher energy were imposed as inputs for kernel-based models. Non-linear neural average (NNA) model was also used for data post-processing. The obtained results from the defined models showed the high accuracy of the integrated methods used in the research in estimating flow discharge. At both stations, the error percentage was reduced by approximately 20 to 25% using the integrated pre-post-processing methods compared to the intelligent kernel based models. It was observed that in the case of river flow prediction based on the station's own data, the RMSE error value of the model decreased from approximately 0.3 to 0.26 and in the case of using the previous station data decreased from 0.44 to 0.33. Due to the high capability and accuracy of the pre-processing methods used in this study, similar studies are recommended in other rivers of the country.

Recently, the amount of rainfall underwent serious changes in different areas, particularly in arid and semi-arid regions. Therefore, estimation and pattern recognition of rainfall in a long term period can give sufficient information to hydrologists and water engineers. In this study, for the first time, long-term rainfall pattern in Anzali city for a 67 years period was simulated using a hybrid model so-called “Wavelet-Adaptive Neuro-Fuzzy Inference System” (WANFIS). Rainfalls of 37-, 20- and 10-years period were applied for training, testing and validation of the numerical model, respectively. Firstly, the optimized membership function of the ANFIS network was obtained using the analysis of the numerical results. In other words, the number of optimized membership function was computed to be equal to 8. Then, the various wavelet families were evaluated which the dmey mother wavelet was introduced as the most optimized wavelet family. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WANFIS models were developed. According to the sensitivity analysis, the superior model and effective lags were identified. The superior model estimated the rainfall with high accuracy. For instance, for validation mode of the superior model, the correlation coefficient, scatter index and Nash-Sutcliffe efficiency coefficient were computed to be 0.962, 0.258 and 0.899, respectively.

Recently, the phenomenon of climate change, drought, the exploitation of the ground water has caused a sharp decline in groundwater levels, which has also led to groundwater related subsidence and desertification. Therefore, reliable prediction of groundwater level for managing these resources is of prime importance. Nowadays wavelet transforms through signal decomposition to time and frequency has created an exceptional method for signal processing, with the help of the transformation of the wavelet that has the ability to split the time series into a multi-dimensional substrate with different scales.
One of the useful characteristics of wavelet transforms is the filtering algorithm, which divids the data into two groups of approximation and details by passing them through the wavelet filter. In this study, monthly groundwater surface, rainfall, and temperature data were used. Using the program code written in MATLAB software, it is necessary to analyze the time series data of all three parameters of temperature, precipitation, and water level. The values ​​of the parameters were selected as inputs and placed in the wavelet function. For analyzing all the parameters, according to various mother-wavelet experiments, and considering the above mentioned point, five mother-wavelets (Haar, Coif, Symlet, Db, Db4) were selected. For this purpose, the program was first implemented for each of the following wavelets with 4 different decomposition levels. After several software runs under specific conditions and scenarios and then comparing them with each other, results were obtained.

River's flow prediction and its flood are the important subjects related to the flood projects, hydroelectric power production and water allocation for agricultural, industry and drinking uses. In the present study, wavelet analysis combined with Artificial Neural Network (ANN) was performed to simulate Gamasiyab River flow, located in Nahavand.The analysis was done in daily and monthly scale. For this purpose, the original time series, using wavelet theory, decomposed to multi time sub-signals. Then, these sub-signals were used as input data to the ANN to simulate flow and then the outputs compared with the ANN’s results. In this study, we used the precipitation, temperature, evaporation and flow data of Varayeneh station during the period of 1969 to 2011.Then, 75% of the data was used for training and 25% was considered for simulating data.
In this research, the amount of N and, L were determined 516 and 2 recpectivly. Also, 1 to 4 decomposition degrees were examined to be more precise. The number of neurons in the first layer depends on the degree of wavelet decomposition. The number of input neurons to the network is “m*(j )” in which “j” is the wavelet decomposition degree and “m” is the number of input parameters. For example, for j=1, according to the input parameters which in this study is 4 (precipitation, flow, temperature and evaporation), the number of input neurons is equal to 8. The output layer also has one neuron. The number of the middle layer neurons is variable and is obtained by trial and error. However, in this study, the number of neurons analyzed in the middle layer was varied from 3 to 20. In this case, modeling was done using different training functions and transfer functions for different neurons of the hidden layers. For daily period, input parameters were also decomposed with wavelet function with the levels of 5 to 9 and used as the input data of ANN. Also, the results concluded that 5 architectures had the best performance and it was also observed that the number of neurons of the middle layer was less than 10 in the superior structures. This means that the optimal solution can be reached with a lower number of neurons. Different training functions were examined, but useing all training functions is not suggested because it is time-consuming. Therefore, as proposed in this article, the three models of “Levenberg-Marquardt”, “BFGS Quasi-Newton” and “Bayesian Regularization” due to their better performance are recommended for future studies. After examining different stimulus functions, it is concluded that four types of “Tansig”, “Logsig”, “Satlin” and “Poslin” stimulus functions are suggested to be used for future studies.
Signal decomposition with wavelet increased correlation between observation and simulation data compared to Artificial Neural Network model, so the R2 was increased by 3 percent during the daily period and 24 percent during the monthly period. Two Artificial Neural Network and Wavelet – Artificial Neural Network (WANN) models in the daily period had almost similar performance but Wavelet – Artificial Neural Network model had a better performance in predicting minimum points. Moreover, after examining different structures, is the results determined that 5 architectures, functioned better in the various models and in the daily and monthly periods of the architecture. It means that using the data of evaporation and temperature makes the performance of the models better than the architecture in which these data are not used. Accordingly, the use of temperature and evaporation parameters is suggested in further studies in addition to flow and precipitation parameters.
The results showed that hybrid model of Wavelet-Artificial Neural Network (WANN) outperformed the Artificial Neural Network model. The reason of this preference of the WANN hybrid model is that the separation affects the time series inputs before interring to the network and primary signal decomposed to the various sub-signals. By doing this, we can use the analysis that contain short term and long term effects.
Besides, wavelet function of “Db4” has a better performance than other wavelet functions in each time period. It has the best performance in the daily period in the 5 level and in the monthly period in the second level. Also, the results showed hybrid model outperformed to estimate extent points than artificial neural network model.

Reference crop evapotranspiration is one of the most important and effective factors for optimizing agricultural water consumption and water resources management. Forecasting of daily reference evapotranspiration can be used for short-term planning of irrigation water requirements. In recent years the use of artificial neural networks and wavelet-neural hybrid model has become very popular in the forecasting of hydrological parameters. The aim of the present study is to use artificial neural network and wavelet-neural network models to forecast reference evapotranspiration in the range of 1 to 28 days ahead at Tabriz synoptic station. For this purpose, a 10-year period (2000 to 2009), 7 years (2000-2006) for training and 3 years (2007- 2009) to test and validation were considered. To create daily reference evapotranspiration time series at the given period, standard equation Penman-Monteith 56 is used. Different combinations of input data (various delays) and various mother wavelets were used. Results of reference evapotranspiration forecasts for a one day ahead, showed that the wavelet-ANN model (RMSE=0.07 mm/day and R=0.999) compared to the artificial neural network model (RMSE=0.69 mm/day and R=0.964) has higher accuracy in forecasting of reference evapotranspiration. The results showed that the use of time delays of 1 to 7 (M7) and 1 to 6 (M6) days can provide the highest accuracy and fewer delays and delays from one year to two years can reduce the accuracy of the models. Comparison of mother wavelets showed that Meyer wavelet due to greater complexity and similarity to the time series of reference evapotranspiration would increase the accuracy of forecasting. To forecast reference evapotranspiration in 2 to 28 days ahead, the wavelet-neural network with Meyer wavelet model was used. The results showed that the increasing of the forecasting period from 2 to 28 days would decrease the accuracy of models (R is ranged from 0.997 to 0.929 for 2 and 28 days ahead). Also, at forecasting 2 to 12 days, annual delays reduced the accuracy of the model, while at 13 to 28-day annual delay increased the accuracy of models. Finally, to compare the models, statistically, t and F tests were performed to compare the mean and variance. The comparison showed that in all the proposed models, at 99 and 95 percent levels, there was no significant difference between the results and observations. The results of this study can be used in irrigation scheduling at study area.

Awareness of flow rate data in rivers is essential for management of water resources, flood forecasting, engineering design and environmental management. The presented models for flow rate predicting in rivers, such as rainfall-runoff and time series are not consistent with the observed data in many cases due to the lack of accuracy and complexity of the factors affecting the discharge. Wavelet is one of the methods that has been considered in recent years in the field of hydrology. Wavelet method is also very effective in the field of signals analysis and time series. This paper presents a hybrid intelligent model based on artificial neural network and wavelet transforms is used to simulate monthly average discharge in Kor River and Pol-e-Khan Station. Performance of prediction models were evaluated using the criteria of Root Mean Square Error (RMSE) and determination coefficient. The results showed that the hybrid model of artificial neural network and wavelet transform with 2 degrees of decomposition offers the best results for the most suitable structure. In this structure, the output discharge for flow rate in the following month is calculated based on discharge in 4, 3, 2 and 1 month ago and current month and the values of RMSE and obtained 7.14 and 0.941 respectively.

In this study the relationship between daily flow decomposed waves of Ajichay river in Vanyar station is investigated using the wavelet and moving average filter. Initially، using wavelet analysis the frequencies distribution of daily flow time series based on scale is decomposed to high and low frequencies and approximate subseries for higher scale are estimated. In the next step moving average filter for decreasing and removing of random noises in daily flow data time series is used. Obtained results represent similar performance of the wavelet analysis and moving average methods in signal smoothing and noise decreasing of Ajichay river daily flow time series for decomposition levels 1 to 10 in wavelet method. Also the quantitative criterion of the correlation coefficient for investigation of the performance similarity of the both methods is used. Results show high correlation between obtained values from the methods used in the study.