Concept of Soil Moisture Ratio for Determining the Spatial Distribution of Soil Moisture Using Physiographic Parameters of a Basin and Artificial Neural Networks (ANNs)

Edyta Kruk and Wioletta Fudała
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Edyta Kruk: Department of Land Reclamation and Environmental Development, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in Krakow, Al. Mickiewicza 24-28, 30-059 Kraków, Poland
Wioletta Fudała: Department of Land Reclamation and Environmental Development, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in Krakow, Al. Mickiewicza 24-28, 30-059 Kraków, Poland

Land, 2021, vol. 10, issue 7, 1-13

Abstract: The results of investigations on shaping the soil moisture ratio in the mountain basin of the Mątny stream located in the Gorce region, Poland, are presented. A soil moisture ratio was defined as a ratio of soil moisture in a given point in a basin to the one located in a base point located on a watershed. Investigations were carried out, using a TDR device, for 379 measuring points located in an irregular network, in the 0–25 cm soil layer. Values of the soil moisture ratio fluctuated between 0.75 and 1.85. Based on measurements, an artificial neural network (ANN) model of the MLP type was constructed, with nine neurons in the input layer, four neurons in the hidden layer and one neuron in the output layer. Input parameters influencing the soil moisture ratio were chosen based on physiographic parameters: altitude, flow direction, height a.s.l., clay content, land use, exposition, slope shape, soil hydrologic group and place on a slope. The ANN model was generated in the module data mining in the program Statistica 12. Physiographic parameters were generated using a database, digital elevation model and the program ArcGIS. The value of the network learning parameter obtained, 0.722, was satisfactory. Comparison of experimental data with values obtained using the ANN model showed a good fit; the determination coefficient was 0.581. The ANN model showed a minimal tendency to overestimate values. Global network sensitivity analysis showed that the highest influence on the wetness coefficient were provided by the parameters place on slope, exposition, and land use, while the parameters with the lowest influence were slope, clay fraction and hydrological group. The chosen physiographic parameters explained the values of the relative wetness ratio a satisfactory degree.

Keywords: soil moisture; physiographic parameters of basins; artificial neural network (ANN); redundancy analysis (RDA) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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