Applying an Artificial Neural Network (ANN) to Assess Soil Salinity and Temperature Variability in Agricultural Areas of a Mountain Catchment

Abstract

Spatial analysis is currently a popular research tool, particularly in studies that focus on soil properties, and it is important for a comprehensive presentation of results by means of spatial statistics techniques. Spatial autocorrelation determines a degree of relationship between variables for two specific spatial units (locations). This relationship is reflected by spatial dependence of investigated soil properties. Moran's I was used as a measure of spatial autocorrelation. Positive spatial autocorrelation was determined for soil salinity (electrical conductivity) and temperature. Thus, the aim of the study was to identify the factors affecting spatial correlation of electrical conductivity (EC) and temperature in farmland and forest-covered areas. A model of artificial neural network was based on salinity, as salinity reduces the amount of nutrients and soil temperature, thus inhibiting plant root growth. Our study revealed that the most effective parameters determining soil temperature were EC and moisture content. The best results in the EC model were achieved for soil moisture content, temperature, and soil texture. Both soil parameters were impacted by catchment land use. Spatial analysis of soil properties and identification of factors affecting their diversity may be helpful in determining proper land use -particularly of sustainable agricultural practices in mountain areas.

Prediction of Algal Chlorophyll-a and Water Clarity in Monsoon-Region Reservoir Using Machine Learning Approaches

“…are using spatially to predict the chlorophyll-a and Secchi depth in the aquatic system. Among these data-driven models, the statistical learning approaches (regression and machine learning) have been widely used to predict chlorophyll-a and Secchi depth in reservoirs all over the world [8,18,19].Regression and machine learning approaches such as multiple linear regression (MLR), support vector machine (SVM) and artificial neural network (ANN) are one of the promising tools to predict the chlorophyll-a and transparency (Secchi depth), which reflect the nonlinearity among chlorophyll-a, Secchi depth and environmental variables using stochastic error minimization techniques. In the past, multiple linear regression method has been used to predict changes in chlorophyll-a and transparency (Secchi depth) with, however, inadequate and unsatisfactory results due to their complex and nonlinear evolution [20].…”

“…are using spatially to predict the chlorophyll-a and Secchi depth in the aquatic system. Among these data-driven models, the statistical learning approaches (regression and machine learning) have been widely used to predict chlorophyll-a and Secchi depth in reservoirs all over the world [8,18,19].…”

Multiannual Assessment of the Risk of Surface Water Erosion and Metal Accumulation Indices in the Flysch Stream Using the MARS Model in the Polish Outer Western Carpathians

“…The spatial relationships between land use exhibit diversity within the entire catchment and the related sources of pollution [40][41][42][43][44][45][46][47]. The physicochemical parameters of water quality can be used to illustrate the general trends related to the hydrogeochemical environment in the catchment [48,49], while water quality indices are used as hydrochemical standards for determining the pollution sources [50,51].…”

Multi-Index Classification Model for Loess Deposits Based on Rough Set and BP Neural Network

“…Therefore, a multi-index intelligent classification method for loess deposits around tunnels is proposed in this study. Although nonlinear methods already have a wide range of engineering applications [19][20][21][22][23][24][25][26][27][28], there is little research on the application of nonlinear methods for evaluating loess deposits.…”