Irrigation
A. Kazemi Choolanak; F. Modaresi; A. Mosaedi
Abstract
IntroductionPredicting 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 ...
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IntroductionPredicting 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. Materials and MethodsIn 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. Results and DiscussionThe 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. ConclusionStreamflow 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.
F. Ahmadi
Abstract
Introduction: Surface water has always been one of the most essential pillars of water projects and, with modeling and predicting the river flow, in addition to the management and utilization of water resources, it is possible to inhibit the natural disasters such as drought and floods. Therefore, researchers ...
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Introduction: Surface water has always been one of the most essential pillars of water projects and, with modeling and predicting the river flow, in addition to the management and utilization of water resources, it is possible to inhibit the natural disasters such as drought and floods. Therefore, researchers have always tried to improve the accuracy of hydrological parameters estimation by using new tools and combining them. In this study, the effect of seasonal coefficients and mathematical methods of signal analysis and signal processing on wavelet transform to improve the performance of the Gene Expression Programming (GEP) model were discussed.
Materials and Methods: In the present study, for the prediction of the monthly flow of Ab Zal River, the information of Pol Zal hydrometric station in period 1972 to 2017 was used. In the next step, different input patterns need to be ready. To this purpose, the data are presented in three different modes: (a) the use of flow data and considering the role of memory up to four delays; (b) the involvement of the periodic term in both linear (?-GEP) and nonlinear (PT-GEP) states, and (c): data analysis using the Haar wavelet, Daubechies 4 (db4), Symlet (sym), Meyer (mey), and Coiflet (coif), was done in two subscales, prepared, and introduced to the GEP model. To better analyze the effect of mathematical functions used in the GEP method, two linear modes (using Boolean functions including addition, multiplication, division, and minus) and nonlinear (including quadratic functions, etc.) were considered. The wavelet transform is a powerful tool in decomposing and reconstructing the original time series. Wavelet function is a type of function that has an oscillating property and can be quickly attenuated to zero. Modeling was done based on 80% of recorded data (432 months) and the validation was done based on the remaining 20% (108 months). To evaluate the performance of each of models, statistical indices such as mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) were used.
Results and Dissection: The results of linear and nonlinear GEP models showed that in both cases, the four-delay model achieved the most accuracy in river flow prediction. Still the performance of nonlinear GEP model according to RMSE (4.093 (m3/s)), MAE (2.782 (m3/s)) and R (0.660) were better than another, respectively. In the next step, the periodic term was added to the model inputs. Based on the results, the PT-GEP model with M4 pattern had the lowest error, the highest accuracy and was able to reduce the RMSE index by 8%. Then, in the third step, the river flow data were divided into approximate subdivisions and details using five wavelet functions. The most appropriate level of analysis based on the number of data was considered as number three. The results of the W-GEP modes showed an excellent performance of this method so that the model was able to reduce the RMSE statistics with 48.6%, 41.2%, and 31.1% compared to the L-GEP, NL-GEP and PT-GEP methods, respectively. Also, the best performance of the W-GEP model with the Symlet wavelet and the decomposition level of one had the highest accuracy (R=0.847) and the lowest error (RMSE =2.898 (m3/s) and MAE =1.745 (m3/s) among all models (35 models) such as linear and nonlinear, seasonal and non-seasonal and wavelet hybrid models.
Conclusion: Based on the results, it can be concluded that the overall use of data preprocessing methods (including seasonal coefficients and wavelet functions) has improved the performance of the GEP model. However, the combination of wavelet functions with the GEP model has significantly increased the accuracy of the modeling. Therefore, it is recommended as the most suitable tool for river flow forecasting.
H. Mirhashemi
Abstract
Introduction: Potential evaporation is the result of the combined effects of several meteorological elements, including air temperature, relative humidity (or vapor pressure for saturation), wind speed, sunshine hours and air pressure. The amount of potential evaporation depends on how these variables ...
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Introduction: Potential evaporation is the result of the combined effects of several meteorological elements, including air temperature, relative humidity (or vapor pressure for saturation), wind speed, sunshine hours and air pressure. The amount of potential evaporation depends on how these variables interact in each climate region. Potential evaporation response of each of these variables depends on the importance that variable plays in the environment. For example, in windy places, the importance of wind speeds in the potential evaporation rate increases relative to places with calm air. By changing each of these meteorological elements, while the rest of the elements react to the given change, the overall effect of these changes and reactions is reflected in the amount of potential evaporation. It is therefore obvious that the potential evaporation response to meteorological variables due to spatial and time variations of these variables is of a complex nature. Materials and Methods: For this study, monthly data of air temperature, air pressure at sea level, wind speed, relative humidity and sunshine hours were used as independent variables and monthly data of evaporation pan at Tabriz Synoptic Station as response or dependent variable. In this study, firstly, the nonlinear and linear relationship between meteorological elements and potential evaporation were identified through Generalized Additive Model (GAM), MARSplines Model, and Generalized Linear Model (GLM), respectively. In the next step, by applying the simplex algorithm on the MARSplines model, the evaporation response gradient levels were determined individually for the meteorological variables. Also, to understand the process of pure evaporation response to each of these variables under different climatic conditions, first three weather conditions based on Tabriz Synoptic Station data were defined in three scenarios as S-1, S-2 and S-3. Then, by controlling and maintaining the meteorological variables under these three scenarios and combining the simplex algorithm with the MARSplines Model, the net evaporation reaction curves for the meteorological variables changes were evaluated. Results and Discussion: The computational results show that in all combinations, the computational error of the GAM model is less than the GLM model. Also considering the significant variables in each model, the combination of temperature, pressure, wind speed and sunshine are considered as the best subset of the effective variables in the distribution of potential evaporation in both models. On the one hand, relative humidity in these two linear and nonlinear models, in combination with other variables, does not show a significant relationship with potential evaporation. The results of the graphs of Splin smoothing components of the GAM model show that the overall effect of temperature on the evaporation is incremental. But the unit amount of this effect increases with increasing temperature. The individual evaporation reaction against air temperature is similar to its combined reaction. It is thus clear that other meteorological variables do not play a significant role in the influence of air temperature on the evaporation gradient. The overall and hybrid effect of air pressure variations on the amount of evaporation is singular and decreasing. Instead, the individual effect of this variable on evaporation is very intense, decreasing, and partly linear. Therefore, the major influence of air pressure on evaporation in the environment is due to the performance of other variables that interfere with the relationship between these two variables. The evaporation hybrid response to wind velocity was also incremental, although the single and nonlinear evaporation response to wind velocity was not significant, but its tendency was to increase its slope with respect to wind velocity changes. Sunny hours also have a net effect on the amount of evaporation. However, the slope of the solitary effect of this variable, like wind speed, is more than its combined effect. Based on the GLM model results, except for relative humidity, the other variables have a significant linear effect on the potential evaporation. Evaporation response to changes in meteorological variables under S-1, S-2 and S-3 scenarios, while accurately determining the interaction of these variables in plotting absolute evaporation, implicitly implying the synergistic role of these variables in determining absolute evaporation. The lowest distance between the absolute values of evaporation under these three scenarios is related to air temperature, which implies less influence of air temperature than the other variables. That is, the effect of each of the meteorological variables on the amount of evaporation depends to a large extent on the relationship of this variable to other meteorological variables, if such a matter is less weighted for temperature. Conclusion: The results of this study show that, except for air pressure, which has an increment-reducing effect on evaporation, other variables have only an incremental influence on evaporation and the intensity of this relationship has changed. This process has resulted in a nonlinear component in the relation of independent variables to evaporation. Since hybrid spline smoothing graphs determine evapotranspiration response to each of the predictor variables by eliminating the effect of other variables, therefore, consideration of the composition of these meteorological variables provides more accurate information on evaporation behavior against environmental changes. Through individually fitting evaporation against these meteorological elements, one cannot find how evaporation works against environmental changes. Comparing individual and combined evaporation responses to meteorological variables, while identifying the net effect of each of these variables, explains why evaporation responses within a given unit differ from changing meteorological variables over different times and locations.
Abazar Solgi; Amir Pourhaghi; Heidar Zarei; Hadi Ansari
Abstract
Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water ...
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Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated.
Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1), the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was given from Fourier transform that was introduced in the nineteenth-century. Overall, concept of wavelet transform for current theory was presented by Morlet and a team under the supervision of Alex Grossman at the Research Center for Theoretical Physics Marcel in France. After the parameters decomposition using wavelet analysis and using principal component analysis (PCA), the main components were determined. These components are then used as input to the support vector machine model to obtain a hybrid model of Wavelet-SVM (WSVM). For this study, a series of monthly of BOD in Karun River in Molasani station and auxiliary variables dissolved oxygen (DO), temperature and monthly river flow in a 13 years period (2002-2014) were used.
Results and Discussion: To run the SVM model, seven different combinations were evaluated. Combination 6 which was contained of 4 parameters including BOD, dissolved oxygen (DO), temperature and monthly river flow with a time lag have best performance. The best structure had RMSE equal to 0.0338 and the coefficient of determination equal to 0.84. For achieving the results of the WSVM, the wavelet transform and input parameters were decomposed to sub-signal, then this sub-signals were studied with Principal component analysis (PCA) method and important components were entered as inputs to SVM model to obtain the hybrid model WSVM. After numerous run this program in certain modes and compare them with each other, the results was obtained. One of the key points about the choice of the mother wavelet is the time series. So, the patterns of the mother wavelet functions that can better adapt to diagram curved of time series can do the mappings operation and therefore will have better results. In this study, according to different wavelet tests and according to the above note, four types of mother wavelet functions Haar, Db2, Db7 and Sym3 were selected.
Conclusions: Compare the results of the monthly modeling indicate that the use of wavelet transforms can increase the performance about 5%. Different structures and sensitivity analysis showed that the most important parameter which used in this study was parameter BOD, and then flow, DO and temperature were important. This means that the most effective BOD and temperature with minimum impact. Also between different kernels types, RBF kernel showed the best performance. So, combined wavelet with support vector machine is a new idea to predict BOD value in the Karun River.
M. Erfanian; S. Babaei Hesar
Abstract
Short wavelength of solar radiation that reaches the ground used as one of the key parameters in most models those estimate the potential Evapotranspiration, such as FAO Penman-Monteith. Despite the importance of amount of radiation, its measurement is done only in small number of stations in the country. ...
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Short wavelength of solar radiation that reaches the ground used as one of the key parameters in most models those estimate the potential Evapotranspiration, such as FAO Penman-Monteith. Despite the importance of amount of radiation, its measurement is done only in small number of stations in the country. Empirical radiation models, such as Angstrom- Prescott, despite the simplicity, require calibration and their coefficients must be properly estimated. In the present study a relatively simple physical model that called hybrid model was used to estimate daily solar radiation in 10 synoptic stations, Esfahan, Bojnourd, Bandar Abbas, Tabriz, Tehran, Ramsar, Zahedan, Kerman, Kermanshah and Mashhad and the results were compared with modified Daneshyar & Sabagh models those were proposed for various climatic conditions in previous researches. using a relatively small number of meteorological parameters include temperature, relative humidity, pressure and sunshine hours, Hybrid model estimated amount of radiation with reasonable accuracy. To compare the three models, mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and mean percentage error (MPE) statistical criteria were used. Average of each error criteria in Hybrid model, respectively are, -0.27, 1.27, 1.59 and 2.01 and in the modified Daneshyar model are, -0.19, 2.64, 3.15 and 3.42. Also in modified Sabagh model these criteria are achieved equal to 0.91, 2.87, 3.93 and 11.20. Small amount of error criteria for Hybrid model represents the relatively high performance of this model in the estimation of solar radiation in daily scale.
