Integration of multiple machine learning models and ultrasonic method to measure soil water content under freeing condition

LIN Xing; WANG Bingliang; CHEN Shujuan; YANG Wu; TONG Yusen; SHEN Fangsen; CHEN Bingtong; LI Wangcheng

doi:10.13522/j.cnki.ggps.2025015

Abstract：[Objective]Methods for measuring soil moisture are many and vary,but most of them are either hindered by low accuracy,high cost,or sensitive to environment.Ultrasonic technology is a non-invasive method with high resolution;it has potential to overcome above limitations.Although ultrasound has been used to measure water content in unsaturated and frozen soils,there is a lack of models to accurately represent the influence of environmental factors on soil water measurement.This paper is to address this gap by integrating ultrasonic method and machine learning(ML)to optimize measurement of soil moisture content under the impact of different environmental conditions.[Method]A laboratory experiment was conducted using soil samples taken from Yinhe Village in Huinong District of Shizuishan,Ningxia.Four ML models,BP neural networks(BP),long and short-term memory(LSTM),extreme learning machines(ELM),and random forests(RF),were combined with ultrasonic method to measured soil ultrasonic properties linked to moisture content.To improve modelling accuracy,the dung beetle optimization[DBO],goose optimization[GOOSE],particle swarm optimization[PSO]and sparrow search algorithm[SSA]were applied to optimize the ELM model.The relationships between ultrasonic wave velocity,temperature and moisture were analyzed in two extreme scenarios:poorly frozen soil with temperatures ranging from-5 to-15℃and soil water content varying from 10%to 20%,heavily frozen soil with temperature ranging from-10 to-15℃and soil water content varying from 25%to 30%.[Result]Above freezing point,the ultrasonic wave velocity slightly increased with soil water content but decreased once soil water content exceeds 15%.For temperature below freezing point and soil water content in the range of 10%to 20%,the ultrasonic velocity was negatively correlated with temperature and decreased sharply as soil water content increased.When soil water content exceeded 30%,the ultrasonic velocity increased significantly as soil water content increased.Among all ML models we compared,the ELM was most accurate prior to optimization,with R2=0.96 and MSE=0.01.After optimization,SSA-ELM was most accurate with R2=0.98 and MSE=0.000 9.For the two extreme scenarios,the SSA-ELM was most accurate;its statistical metric for Scenario 1 were R2=0.98 and RMSE=0.02,and R2=0.97 and RMSE=0.06 for Scenario 2.[Conclusion]The SSA-optimized ELM model combined with ultrasonic technology is most accurate for estimating soil moisture content,even under extreme freezing condition.