Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promi...

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Bibliographic Details
Published in:Journal of hydroinformatics 2009-07, Vol.11 (3-4), p.237-251
Main Authors: Elshorbagy, Amin, El-Baroudy, Ibrahim
Format: Article
Language:English
Subjects:
Atmospheric conditions
Atmospheric models
Biological evolution
Capacity
Current measurement
Data
Environment models
Environmental restoration
Evolution
Genetic algorithms
Hydrologic cycle
Hydrologic models
Hydrological cycle
Hydrology
Measurement methods
Mining
Mining industry
Modelling
Moisture content
Restoration
Soil
Soil investigations
Soil layers
Soil moisture
Storage
Studies
Temporal variations
Thickness
Watersheds
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Summary:Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.
ISSN:1464-7141
1465-1734
DOI:10.2166/hydro.2009.032