Generation of Karun River Water Salinity Map from Landsat-8 Satellite Images using Support Vector Regression, Multilayer Perceptron and Genetic Algorithm

Introduction:

The Karun River is the biggest river basin in Iran, which supplies water demands of about 16 cities, several villages, thousands of hectares of agricultural. This river polluted because of domestic and urban sewerage, industrial sources, and irrigation of agricultural land, Hospital sewage and high tide level of Persian Gulf.
Therefore, because of the importance of this river, the water salinity of this river is determined in this study. The traditional methods of determining water salinity are costly in comparison with remote sensing methods.
In the present study, Landsat 8 (OLI) data was used to calculate the water salinity map for Karun River since not only it is free, but it also has an acceptable resolution.


Materials and Methods:

Landsat 8 (OLI) images were used to calculate reflectance for a pixel and were attained from (US Geological Survey (USGS) 2019). First, radiometric correction was applied to normalize satellite images. This process convert Digital Number into radiance. Second, in order to attain the surface reflectance values, the process of atmospheric correction was applied using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH).
Water salinity was calculate by Iran Water and Power Recourses Development Company. Eight stations are located in the crucial point for EC measuring ALIKALE, GOTVAND, SHOOSHTAR, SHOTEYT, GARGAR, DEZ, AHVAZ, and ABADAN.
Iran Water and Power Recourses Development Company obtained 102 observed EC samples from June 2013 to July 2018 along the Karun River.
The Support Vector Machine was classically used for classification, Support Vector Classification, but extended for using along with regression issue, namely Support Vector Regression.
The results related to the quality of the SVR depend on some factors: the loss function Ɛ, the error penalty factor C and the kernel function parameters.
Usually, radial basis kernel function (RBF), k(x, x΄) = k(x,x΄)=exp⁡〖( -||x-x΄〗 2/σ^2), has been used in remote sensing studies, so, it is implemented in this study. Finally, the Genetic Algorithm (GA) is employed to optimize some parameters including C, Ɛ and σ.
GA is an optimization technique create by Holland (1975) and discussed the mechanism of GA in solving nonlinear optimization problems.
Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy.


Results and Discussion:

Salinity intrusion is a complex issue in coastal, hot, and dry areas. Currently, remote sensing techniques have been widely used to monitor water salinity changes, ranging from inland river networks to deep oceans. The Karun River basin, with a basin area of 67,000 km^2, is located in the southern part of Iran. The salinity of Karun River has been increasing due to some critical factors, e.g. severe climate condition and regional physiography, industrial sources, domestic and urban sewerage, irrigation of agricultural land, fish hatchery, hospital sewage, and high tide level of Persian Gulf .
This study aimed at building Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. 102 observed samples were divided into 75% training and 25% test.
Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy.
The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R^2) and RMSE of test data is obtained as 0.73 and 390μs 〖cm〗^(-1).
GA analysis proved that bands 1, 2 and 3 are the best for modeling water salinity. In this study, the GA is used to determine the SVR meta-parameters including the loss function Ɛ, the error penalty factor C and σ parameters, which are obtained to be〖1×10〗^(-9), 1099 and 0.96, respectively, and number of layers and neurons of MLP neural network, which are obtained to be 5 and 35, respectively.
The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R^2) and RMSE of test data is obtained as 0.73 and 390μs 〖cm〗^(-1).

Conclusion:

The present study calculated the relationship between reflectance retrieved from Landsat-8 OLI and water salinity in the Karun River. SVR and MLP models had acceptable operation by considering the large size, geographic complexity of the study domain and the wide range of field data that change between 385 and 4310μs cm^(-1). Augmentation field data is the critical priority work for future study to probe the relationship between water salinity and satellite images.In addition, the contribution of thermal bands can help to increase accuracy of models. Salinity intrusion is a complex issue in coastal and hot and dry areas. Currently, remote sensing techniques have been widely used to monitor water salinity changes, ranging from inland river networks to deep oceans. The Karun River basin, with a basin area of 67,000 km2, is located in the southern part of Iran. The salinity of Karun River has been increasing due to some critical factors, e.g. severe climate condition and regional physiography, industrial sources, domestic and urban sewerage, irrigation of agricultural land, fish hatchery, hospital sewage, and high tide level of Persian Gulf .This study aimed at building Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models to realize the salinity intrusion through the relationship between reflectance from the Landsat-8 Operational Land Imager images and salinity levels measured in situ. A total of 102 observed samples were divided into 75% training and 25% test. Besides, the Genetic Algorithm (GA) was applied to determine the best performer bands combination. Furthermore, we employ GA to optimize SVR parameters and number of layers and neurons of MLP neural network in order to maximize model accuracy. The result showed that the MLP approach was the better model to estimate water salinity along the Karun River network in the study area, which coefficient of determination (R2) and RMSE of test data is obtained as 0.73 and 390μscm-1