preprocessing

Hydrological time-series is a time-dependent hydrological variable that finding the model of changes and predicting is the most important goal of time-series analysis. The purpose of this study is to simultaneously study the characteristics of time series and their prediction and the important parameters of the GEP for high-precision predictions in the training and validation. In this study, groundwater depth time-series of Chamchamal plain station located in Kermanshah province with a 12-year period and mountainous climate and the monthly time-series of Alaska temperature with a 50-year period and cold and dry climate have been used. Genexprotools5.0 software has been used to model time-series by GEP.The results of studying with GEP showed that the periodicity of data properties that existed in the time series of temperature caused correlation results above 90% in different stages of training and validation. So that the effect of different parameters of GEP is less than 10% in improving results. On the other hand, by examining the time-series of groundwater depth, which lacks periodicity and has a descending ACF shape, the prediction results of the GEP with any effective expression parameter, R more than 44% in the validation wasn't obtained. This means that the time-series preprocessing has a greater impact on the prediction results. So that by eliminating the semester, the prediction results in all stages of modeling are significantly reduced. In this case, the best R for the validation is 50%.

For sustainable land management, it is necessary to understand the functions and characteristics of soils, and their spatial and temporal changes. Visible and near-infrared spectroscopy has a specific capability to identify and determine soil properties due to high accuracy and high-performance speed. The purpose of this study is to evaluate the accuracy of visible and near-infrared spectroscopy method in estimating soil organic matter and total neutralizing value. Therefore, 110 soil samples were collected from Khuzestan, Yazd and Tehran provinces, and spectral reflectance was performed using ASD FieldSpec3. The spectra obtained from the spectrometer were pre-processed using five methods including Savitzky-Golay filter (SG), the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variate (SNV), and Multiplicative scatter correction (MSC). Also, the performance of PLSR and SVMR methods was compared in terms of soil organic carbon and total neutralizing value estimation. The results indicated that the PLSR model in estimating both organic carbon properties and total neutralizing value had higher accuracy compared to the SVR model. In estimation of soil organic carbon, PLSR method and MSC preprocessing method had the best performance (R2VAL=0.59, RMSEVAL=0.19 and PRDVAL=1.47) and the second derivative method had the least performance (R2VAL=0.15, RMSEVAL=0.27 and PRDVAL=0.52). Also for estimation of total neutralizing value, the first derivative preprocessing method had the best performance (R2VAL=0.78, RMSEVAL=5.70 and PRDVAL=2.01) and the second derivative method had the least performance (R2VAL=0.1, RMSEVAL=11.13, and PRDVAL=0.31). The key wavelengths were observed for soil organic matter in the range of 421- 612 nm and for total neutralizing value in the range of 2315- 2151 nm. This study showed that the Vis-NIR spectroscopy method, due to its physical basis and considering the influencing factors, as a large-scale model, makes it possible to evaluate and predict soil OC and TNV.

Threshold friction speed is an important factor for assessing the soil erodibility, but its measurement is time consuming and costly. Estimating threshold friction velocity by use of soil reflectance increases operating speed and reduces cost. The aim of this study was to compare the efficiency and accuracy of partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) regression models in estimating the threshold friction velocity in dust-prone areas of Khuzestan Province. For this purpose, 91 soil samples were collected from the central and southern parts of dust-prone areas of the province and the threshold friction velocity was determined using wind tunnels. Then, the reflectance spectra of soil samples were obtained with a spectrometer. Pre-processing methods were performed on the main spectrum and modeling was performed using, PLSR, SVR and ANN models. The results showed that the threshold friction velocity in the region was 9.7 m/s and the minimum was 5.25 m/s.  Also, the threshold friction velocity was significantly (p<0.05) correlated with dissolved sodium (r= -0.58) and sodium adsorption ratio (R= -0.48). The ANN model had the best estimation accuracy in the second derivative preprocessing (PRD = 2.52) and the SVR model had the lowest estimation accuracy in the main spectrum (PRD = 0.56). Finally, the key wavelength of the threshold friction velocity was in the range of 1850 and 1930 nm. Because of the soil reflectance correlation with threshold friction velocity (r=0.76), the spectroscopy method can be used to assess the soil erodibility in areas prone to dust production.

Forecasting stream flow is very important for planning and managing water resources systems and its precision has a significant role in accurately implementing the planned objectives. Besides, soft computing has a significant ability in modelling hydrologic processes. Therefore, in the present study, the Hybrid Wavelet-Gene Expression Programming model has been developed in comparison with its singular approach so that it forecasts the daily streamflow of Khoshkroud river located in Guilan province. For this purpose, in addition to the process of pre-processing hydrometric data, the effect of meteorological parameters on the model’s performance and efficiency has been studied. Also, pre-processing was performed with different properties and for four durations of one, two, three and six days. Correlation coefficient (R), index of agreement (Ig), Nash-Sutcliffe coefficient (NSE), mean absolute error (MAE), root-mean-square error (RMSE) and peak flow criteria(PFC) statistical indices were used to assess the models’ performances. The results show that using wavelet transform to pre-process hydrometric data will significantly improve the efficiency of the hybrid model in comparison with the singular model, such that the correlation coefficient of the validation data for three days has increased from 0.27 to 0.80 and similarly, the mean absolute error has decreased from 1.4 to 0.80 m3/Sec. On the other hand, meteorological parameters have caused the extreme values in the river’s flow rate time series to be well modelled and their efficiency in the extreme values to be significantly increased. The results obtained from this research express that the hybrid model alongside the meteorological parameters can be successfully and efficiently used in flow forecasting.

In recent years, due to the lack of surface coating and low soil resistance to wind erosion, the large area of Khuzestan province is sensitive to dust production. Among the soil characteristics, the organic matter by collecting soil particles, has important role in soil resistance to wind erosion and dust production. Since these areas are so wide, the use of traditional methods of soil analysis is really costly and time consuming. The spectroscopy approach, due to the advantage of speed and easy movement, can reduce the cost and time of measurement. The aim of this study is to investigate the spectral behavior of soil organic carbon in central and southern regions of Khuzestan province by using tow multivariate regression, Support Vector Regression) SVR( and neural network (PLS-ANN), and key wavelength determination of soil organic matter in these areas.

In this research, the study area was divided into 2 km square grids and systematic and random sampling Methods were performed. The soil organic matter in samples was measured in the laboratory. The Reflectance spectra of soil samples were determined using FildSpect setup in dark room. And spectral measurements were carried out with three types of detectors in range of visible to near infrared (3500-2500 nm). To eliminate the noise in normal reflectance spectra, the main spectra were preprocessed by four methods, including the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variant method (SNV) and the continuum removed method (CR). Next, the performance of SVR and PLS-ANN models in main spectra preprocessed method were compared.

The results showed that the PLS-ANN model had better accuracy compared to SVR model in estimating organic carbon. In SVR models, the continuum removal method (CR) had the best performance (R2CAL=0.84, RMSECAL=0.06 and RPDCAL=1.82) and the main Spectra had the worst performance (R2CAL=0.74, RMSECAL=0.14 and RPDCAL=1.66). In PLS-ANN models, the best performance belonged to the second derivative (SD-SG), (R2CAL=0.92, RMSECAL=0.05 and RPDCAL=2.34) and the worst performance was related to the first derivative (FD-SG), (R2CAL=0.80, RMSECAL=0.1 and RPDCAL=1.86).

In this study, the preprocessing methods improved the overall accuracy of SVR and PLS-ANN models compared to the main spectrum. According to the second derivative method, in PLS-ANN witch had the best accuracy in estimating soil organic carbon, the Wavelength ranges around 800, 1800 and 2000 nm were identified as the key wavelength of the organic carbon in sensitive centers to dust production.

A broad area of saline and semi-saline lands of Khuzestan province have changed into centers susceptible to dust production due to eroded wind and lack of surface coating and low soil resistance. The objective of this study was to model the soil salinity of sensitive areas to dust production in Khuzestan Provenience usin spectrometry method of visible and near-infrared wavelengths (2500-350 nm). The least square multivariate regression model, artificial neural network and random forest model were used to estimate soil salinity. The main soil spectrum was determined using the FieldSpect machine. Also, preprocessing methods including Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normalization method (SNV), and the continuum remove method (CR) were used to eliminate the noise and to increase the accuracy of the multivariate model. The results showed that the combined model partial least squares-artificial neural network model with assessment criteria (RPDcal = 3.40-2.65) has high accuracy for salinity estimation. In contrast, the combined model of least squares - random forest showed the lowest accuracy (RPDcal = 0.85-1.98). Preprocess of the main spectrum in two models (neural network and partial least squares regression) increased the relative accuracy of the model; while in the random forest model, preprocess reduced the accuracy of the model compared to the main spectrum. The ranges of 1800, 1900, 2000, 2300 and 1500 nm were recognized as "the key wavelengths" impressed by soil salinity. The key wavelengths can be used in remote sensing studies and mapping of soil salinity in areas sensitive to dust production in Khuzestan province.

Soil organic matter is considered as an indicator of soil quality and its fertility due to its impact on the physical, chemical and biological properties of soil. The purpose of this study was to investigate the effect of moisture on soil spectral behavior in order to estimate the amount of organic matter using visible-near infrared spectroscopy. Also, the performance of different preprocessing methods was compared in modeling. For this purpose, 50 soil samples from a surface layer (0-30 cm) were randomly collected from some cities in Tehran province. The samples were air-dried and passed through a 2 mm sieve. The amount of organic matter and soil texture were measured in the laboratory. To measure spectral reflectance, soil samples are dry in the oven and they were hydrated with levels of 5, 10, 15 and 20 wt.% (Grams of water / g of soil). Then, Soil samples were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The results of the cross-evaluation indicated that SNV pre-processing method was better in predicting organic carbon content of soil. The best result for the validation group was obtained in dry oven moisture with R2 and RMSE values of 0.83 and 0.422 respectively. Also, R2 and RMSE were 0.75 and 0.543, respectively, at 5% moisture content, 0.70, 0.553 at 10% moisture, 0.605, 0.558 in moisture content of 15%.

Optimal use of irrigation water requires to the precise irrigation planning and the accurate crop coefficient estimation is the prerequisite of that particularly in the global scale. The aim of research was the comparison of preprocessing approaches of artificial neural network: regression and data reduction (principle component analysis and rotation) for crop coefficient estimation using NDVI, RI, TVI, MSAVI, SAVI, mTVI, EVI, MNDVI and TVX for wheat crop coefficient of East Azarbaijan Province. The performance of regression and data reduction approaches indicated the error criteria decreasing of data reduction approach, for example RMSE increasing from rotation to regression and from principle component analysis to regression was 11.8 and 22.7%, respectively. The used approaches of crop coefficient estimation has overestimation as the average increase of crop coefficient in the validation period showed 7.7, 6.13 and 4.6% increasing of crop coefficient from FAO to the regression, principle component and rotation approaches, respectively. Therefore, using the rotation in the data reduction analysis increased the accuracy of estimation. Decreasing of correlation coefficient-39.13% - from MSAVI to NDVI indicated that the improved indices basis on the study area condition increased the performance of crop coefficient estimation using satellite images.