Performance assessment of PSO and GA in estimating soil hydraulic properties using near-surface soil moisture observations
Keywords:
Genetic algorithm, Particle Swarm Optimization, soil propertiesAbstract
Quantification of soil moisture movement and water uptake dynamics in the vadose zone for sound irrigation management requires the knowledge of soil hydraulic properties. Non-availability of complex and expensive instrumentation hinders identification of soil hydraulic and retention characteristics. The study presents the application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in identifying soil moisture retention θ(h) and hydraulic conductivity K(h) functions by inverting a SWAP model using observed near-surface soil moisture (0-10 cm). Two hydrologic cases, i.e. homogenous soil column with free drainage and with Shallow Groundwater Table (SGT) at the lower boundary, are considered. Study takes into account the agro-climatic data of Palampur (Himachal Pradesh), India. Results for both cases establish the applicability of GA and PSO in identifying soil hydraulic parameters. The identification of soil hydraulic parameters is more accurate when the soil column is draining in comparison to that with SGT. The comparative evaluation of simulated to the field observed soil moisture content indicates root mean square error of 0.0163 and 0.0297 for GA and PSO respectively. GA provides an effective alternative to estimate soil hydraulic properties using inverse approach in absence of experimental values.
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