جستجوی مقالات مرتبط با کلیدواژه « صحت سنجی متقاطع » در نشریات گروه « آب و خاک »

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.

The present study demonstrates a method in groundwater quality assessment using the Empirical Bayesian Kriging and Moran Spatial Autocorrelation Index. In this study, concentration of Arsenic, Lead, Iron, Manganese and Nitrate and also groundwater table and Total Dissolved Solid has been measured for 21 point in Azarshahr Plain (East Azerbaijan). Azashahr study area is one of the Lake’s twelve adjacent aquifers that is located between 45°,46’ to 45°,50’ longitudinal and 37°,43’ to 37°,52’ latitudinal. Its total area is about 457 km2, that its plain has an area about 124 km2. The highest and lowest heights in the study area are 3700m and 1282m, respectively. Its average annual precipitation is about 221.2 mm whereas the average annual evaporation is about 1579 mm. The most important stream in Azarshahr Plain is Azarshahrchai which has a southeast-northwest trend and is eliminated before reaching to the lake because of wide agricultural usage. On the other hand, the total annual discharge of aquifer is about 90.64 million that is one of the groundwater depletion and decreasing the quality of groundwater factors in the study area. Moran’s I is a commonly used indicator of spatial autocorrelation. In this study, the Moran’s I was used as the first measure of spatial autocorrelation. Its value ranges from −1 to 1. The value “1” means perfect positive spatial autocorrelation (high values or low values cluster together), while “−1” suggests perfect negative spatial autocorrelation (a checkerboard pattern), and “0” implies perfect spatial randomness. After that, the appropriate weight has given to the aforementioned parameters, considering the international standard of water quality and spatial autocorrelation index of each of them. After determining the layer rules, the Expert Choice software was applied to calculate the comparing binary matrix of analytic hierarchy process. After that, the final weight for each layer with inconsistency of 0.08 was derived that is less than 0.1 and acceptable. In the conventional geostatistical approaches for interpolation and kriging, the variance structure is estimated first, and then the estimated variance is used for interpolation that whereas a Bayesian approach to the interpolation of spatial processes will provide a general methodology for taking into account the uncertainty about parameters on subsequent predictions. The Bayesian approach generalizes automatically to the case which the variogram parameters are unknown, whereas the classical approach essentially makes the assumption that these are known and only deals with the question of uncertainty of model parameters in a very peripheral way. Then replacing the popular interpolation methods, the Empirical Bayesian Kriging prediction method has utilized to expand every parameter to the whole plain. In order to evaluate the prediction results, the cross validation method was used. The study area was divided to 4 sections, as desirable, acceptable, moderate and non-acceptable. The final obtained map reveals that desirable quality is just located in the southeast of the study area in the upstream of the groundwater input. The acceptable quality of the groundwater is located in the east and southeast of the study area. The center, west and northwest of the study area has a moderate quality. The groundwater in the north, northwest and southwest of the study area has a non-acceptable quality that seems due to anthropogenic activities, especially agricultural and industrial during the recent years. On the other hand, the spatial autocorrelation model of effective parameters on water quality in the on hand and Bayesian kriging method with its precise assessment and prediction in some areas without data have a high applicability. The cross validation technique in model accuracy approving, is a valuable tool. Every three methods played an important role in modifying and improving the analytic hierarchy process of the groundwater quality assessment in the study area. The resulted map revealed that the groundwater quality of the east and southeast of the study area are desirable and acceptable with about 32.53 and 44.38 km2, respectively. The center and west section with area about 69.32 and the north and southwest with area about 27.21 km2 have a moderate and non-acceptable quality, respectively. Upon to the analytic hierarchy process of the groundwater quality assessment of the Azarshahr Plain, it has been revealed that 16 percent of aquifer has non-acceptable quality, about the 40 percent has moderate quality and other has an acceptable to desirable quality in the study area. The result of this study has shown the necessity of the groundwater quality precise monitoring in the study area.

Soil moisture retention curve is one of the soil hydraulic prosperities which its direct measurement is time consuming and expensive. Therefore, indirect methods such as developing pedotransfer functions have been used to predict this characteristic from soil readily available or easily measurable data. In this study, multiple linear regression method was used to develop point pedotransfer functions (PTFs) for saline and saline-alkali soils of Iran. For this purpose, 68 soil samples with EC values greater than 4 dS/m of which more than half of them had ESP values greater than 15% were selected. Using Cross validation method, the random splitting of data into the development and validation subsets was repeated 10 times. A ratio of 3:1 was used to split data into development and validation sets in each replication. In the SPSS software, parameters such as geometric standard deviation (δg), geometric mean diameter (dg), sodium adsorption ratio (SAR), electrical conductivity (ECe), carbonate calcium (CaCO3), bulk density (BD), organic matter (OM), clay and sand content were applied as the independent variables, and volumetric water content was determined at matric potentials of 10, 33, 100, 300, 500, 1000, 1500 kPa. The derived PTFs were compared with the H3 model of Rosetta software for 10 splits of validation data set. Comparison of the mean RMSE and R2 values showed that the developed PTFs resulted in more accurate estimation than the Rosetta software at matric potentials of 100, 300, 500, 1000, 1500 kPa. Whereas, Rosetta model resulted in slightly better estimation than derived PTFs at matric potential of, 33 kPa. For the PTFs developed in this study, the RMSE, ME and R2 values ranged from 1.61 to 4.17 (cm3.cm-3), -0.181 to 0.66 and 0.52 to 0.76, respectively. While for the Rosetta model, RMSE, ME and R2 values ranged from 2.82 to 4.27 (cm3.cm-3), -2.65 to 0.094 and 0.37 to 0.74, respectively.