Chronological assessment of heuristic data driven approaches for soil water content simulation in subsurface drip irrigated rice

Abstract

Accurate estimation of soil water content (SWC) is essential for effective agriculture and water resources management. While various methods have been developed for in-situ SWC measurement, practical limitations and the need for comprehensive water sensor networks make their use complicated. To overcome these challenges, heuristic data-driven models may provide a suitable alternative to practical methods for SWC simulation under different cultivation conditions. In this paper, the application of gene expression programming (GEP) methodology was proposed to simulate SWC at three different depths in rice fields using information related to weather and groundwater. A modeling study was conducted that applied the robust k-fold testing data assignment method, considering two different chronologic strategies of "k" defining to evaluate both strategies. The first one was based on the definition of the "k" values based on yearly data partitioning, while the second one considered growing stages as the "k" definition criterion. Besides evaluating the models using error statistics, a further uncertainty analysis was also conducted to check stability and confidence. The obtained results revealed that selection of "k" based on growing stages produced more accurate and stable results. Among the target parameters, water content at the third layer was predicted with higher accuracy.

Keywords: K-fold testing; Machine learning; Meteorological data; Soil water content; Subsurface drip irrigation.