Irrigation
S.M. Saghebian
Abstract
Introduction: Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. So far, various and complex relationships have been proposed to predict the amount of suspended sediment transport rate, such as velocity and critical shear stress based ...
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Introduction: Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. So far, various and complex relationships have been proposed to predict the amount of suspended sediment transport rate, such as velocity and critical shear stress based equations. However, the complex nature of sediment transport and lack of validated models make it difficult to model the suspended sediment concentration and suspended sediment discharge carried by rivers. Although the developed models led to promising results in sediment transport prediction, due to the importance of sediment transport and its impact on hydraulic structures it is necessary to use other methods with higher efficiency. On the other hand, in recent years, the Meta model approaches have been applied in investigating the hydraulic and hydrologic complex phenomena. Hybrid models involving signal decomposition have also been shown to be effective in improving the prediction accuracy of time series prediction methods, as indicated in. Complementary Ensemble Empirical Mode Decomposition analysis is one of the widely used signal decomposition methods for hydrological time series prediction. Decomposition of time series reduces the difficulty of forecasting, thereby improving forecasting accuracy.In this study, due to the complexity of the sediment and erosion phenomenon and the effect of different parameters in estimating, time series pre-processing methods along with support vector machine (SVM) and Gaussian process regression (GPR) kernel based approaches were used to estimate suspended sediment load of a natural river at two consecutive hydrometric stations. For this purpose, different models were defined based on hydraulic and sediment particles characteristics. Moreover, the capability of integrated pre-processing and post-processing methods in two states of inter-station and between-stations was investigated. First, the Wavelet Transform (WT) method was used for data pre-processing then, the high-frequency sub-series were selected and re-decomposed using the Empirical Mode Decomposition (EMD). Finally, the most effective sub-series were imposed as inputs for kernel-based models. In addition, to assess the reliability of the superior model, Monte Carlo uncertainty analysis was used.The results showed that the GPR model had a desirable degree of uncertainty in modeling.Materials and Methods: In this study, data of two stations of Housatonic River was used. The distance between stations was approximately 50 km. The first station is located near Great Brighton, Massachusetts, and the second station is in Connecticut. The basin area for the stations is 282 and 634 square miles, respectively. The flow path is from the first station to the second station. SVM and GPR models are based on the assumption that adjacent observations should convey information about each other. Gaussian processes are a way of specifying a prior directly over function space. This is a natural generalization of the Gaussian distribution whose mean and covariance are a vector and matrix, respectively. Due to prior knowledge about the data and functional dependencies, no validation process is required for generalization, and GP regression models are able to understand the predictive distribution corresponding to the test input. Wavelet Transform (WT) uses a flexible window function (mother wavelet) in signal processing. The flexible window function can be changed over time according to the signal shape and compactness. After using WT, the signal will decompose into two approximations (large-scale or low-frequency component) and detailed (small-scale component) components. EEMD was proposed to solve the mode mixing issue of empirical mode decomposition (EMD) which specifies the true IMF as the mean of an ensemble of trials. Each trial consists of the decomposition results of the signal plus a white noise of finite amplitude. EMD can be used to decompose any complex signal into finite intrinsic mode functions and a residue, resulting in subtasks with simpler frequency components and stronger correlations that are easier to analyze and forecast. Another important feature of empirical model of decomposition is that it can be used for noise reduction of noisy time series, which can be effective in improving the accuracy of model predictions. In the uncertainty analysis method, two elements are used to test the robustness and to analyze the models uncertainty. The first one is the percentage of the studied outputs which are in the range of 95PPU and the next one is the average distance between the upper (XU) and lower (XL) uncertainty bands. In this regard, the considered model should be run many times (1000 times in this study), and the empirical cumulative distribution probability of the models be calculated. The upper and lower bands are considered 2.5% and 97.5% probabilities of the cumulative distribution, respectively.Results and Discussion: In order to evaluate and review the performance of the tested models and determine the accuracy of the selected models, three performance criteria named Correlation Coefficient (CC), Determination Coefficient (DC), and Root Mean Square Errors (RSME) were used. The obtained results indicated that the accuracy of the applied integrated models was higher than the single SVM and GPR models. The use of integrated methods decreased the error criteria between 20 to 25 %. The obtained results for the uncertainty analysis showed that in suspended sediment load modeling the observed and predicted values were within the 95 PPU band in most of the cases. Moreover, it was found that the amount of d-Factors for train and test datasets were smaller than the standard deviation of the observed data. Therefore, based on the results, it could be induced that the suspended sediment modeling via integrated WT-EEMD-GPR model led to an allowable degree of uncertainty.Conclusion: Comparison of the developed models’ accuracy revealed that integrated GPR and SVM models had higher performance compared with single GPR and SVM models in predicting the suspended sediment discharge. The use of these two methods approximately decreased the error criteria between 20 to 25 %. According to the results, for the models that were developed based on the station data, the model with the input parameters of Dwt, Dwt-1, and Dst-1 and in the case of investigating the relationship between the stations, the model with the input parameters of Dst-2, Dwt-1, and Dst-1 were superior models. Also, based on the uncertainty analysis, the integrated GPR model had an allowable degree of uncertainty in suspended sediment modeling. However, it should be noted that the used methods are data sensitive models. Therefore, further studies using data ranges out of this study and field data should be carried out to determine the merits of the models to estimate suspended sediment load in the real conditions of flow.
Saghar Fahandej saadi; Masoud Noshadi
Abstract
Introduction: Although the soil salinity as an effective factor on soil and water management is typically assessed by measuring the soil electrical conductivity (ECe), this conventional laboratory method is time-consuming and costly. Therefore, near-infrared spectroscopy (NIR) as a fast, cheap and non-destructive ...
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Introduction: Although the soil salinity as an effective factor on soil and water management is typically assessed by measuring the soil electrical conductivity (ECe), this conventional laboratory method is time-consuming and costly. Therefore, near-infrared spectroscopy (NIR) as a fast, cheap and non-destructive method to assess soil salinity level can be considered as a valuable alternative method. Reviews of literature on the application of NIR spectroscopy for soil salinity prediction have shown that there is no sufficient information about the effect of soil texture on results accuracy; therefore, in this study the soil salinity was predicted under different soil salinity levels and various soil textures. The effect of different pre-processing methods was also investigated to improve the predicted soil salinity.
Materials and Methods: Twenty three surface soil samples were collected from different places in Fars province, then; some soil properties such as percentage of particles size and ECe were measured. These samples were artificially salted by adding the water in different salinity levels to the soil samples. The ECe of these soils were between 2.1 to 307.5 dS/m and then all samples dried to reach the field capacity level. Soil reflectance spectra were obtained in 350-2500 nm wavelength range. The absorbance and derivative of reflectance spectra were calculated based on the reflectance spectra. In order to determine the effect of smoothing technique, as a pre-processing method, 4 various methods (moving average, Gaussian, median and Savitzky-Golay filters) in 12 different segment sizes (3,5,7,9,11,13,15,17,19,21,23 and 25) were applied and the processed spectra introduced to Partial Least Square Regression (PLSR) model to predict soil salinity in two calibration and validation steps. At the first step, the soil salinity was predicted for all samples using of reflectance, absorbance and derivative of reflectance spectra under 4 pre-processing methods and 12 segment sizes. According to the R2 and RMSE indices, the best type of spectra, the effect of various pre-processing methods and the best segment size in prediction of soil salinity were determined as absorbance spectra, moving average and Savitzky-Golay filters for segment size of 25 and 15, respectively. In the second step, the effect of soil texture on prediction accuracy was investigated. For this purpose, soil samples were divided into the coarse and fine textures and soil salinity was predicted for each of these groups using different pre-processing methods and different segment sizes.
Results and Discussion: In prediction of soil salinity by absorbance, reflectance and derivative of reflectance spectra, the R2 values in validation step were 0.742, 0.706 and 0.670; and RMSE values were 29.92, 31.96 and 33.9 (dS.m-1), respectively. The absorbance spectra were the best spectra type in prediction of soil salinity. Therefore, in next step, absorbance spectra were used only for predicting the salinity in fine and coarse soil textures. Results showed that the prediction in coarse texture was better than that of the fine texture (R2= 0.836 and R2=0.756, respectively). It was also revealed that the highest R2 occurred in coarse texture and the accuracy of prediction was reduced in fine textures. The results showed that the performance of different pre-processing methods is related to the spectrum type. Although the pre-processing methods had no positive effect in using of reflectance spectra, but it improved the predicted values which were obtained using of absorbance and derivative of reflectance spectra. The best results were occurred when the absorbance spectra were used. Moving average method increased the accuracy of prediction more than the other pre-processing methods, and according to the results this method, for the segment size of 25, was the best technique in soil salinity prediction.
Conclusion: According to the R2 and RMSE indices, the prediction of soil salinity by absorbance spectra was more accurate than the prediction using reflectance and derivative of reflectance spectra (R2= 0.742, 0.706 and 0.670, respectively). Although the predicted soil salinity in coarse soils were more accurate than that in fine soils. Using of absorbance spectra to predict the soil salinity in all soil textures was efficient. The results showed that using of pre-processing methods improved the soil salinity prediction by absorbance and derivative of reflectance spectra, and the moving average and Savitzky-Golay filter were the best pre-processing methods.
