Prediction of soil temperature based on SSA-DELM algorithm for conservation tillage in black soil

Abstract

[Objective] Accurate prediction and analysis of soil temperature in the tillage layer of farmland is of great practical significance for agricultural production, in order to effectively and accurately analyze the characteristics of soil temperature change in the farmland of conservation tillage of black soil lacking in real measurement conditions. [Methods] Based on the Extreme Limit Training Machine(ELM) algorithm, the Self-Encoder Algorithm(AE) was introduced to form the Depth Extreme Limit Training Machine(DELM), which was improved by using the Sparrow Search Algorithm(SSA), and a hybrid SSA-DELM model was constructed to predict soil temperatures at different depths under two types of no-tillage no-straw cover(NT0) and no-tillage full straw cover(NTS) tillage conditions by utilizing the data of meteorological factors, and then predict soil temperatures at different depths under no-tillage no-straw cover(NTS). Soil temperature was predicted and compared with ELM, RF and SSA-RF models. [Results] The results showed that the coefficient of determination(R²) of the SSA-DELM model was 0.996 and 0.998, the mean absolute error(MAE) was 0.16 and 0.1, the root mean square error(RMSE) was 0.29 and 0.16, and the coefficients of Nash′s efficiency(NSE) were 0.999 and 0.999 for the prediction of soil temperatures under two types of tillage conditions, respectively. Performance Index(PI) was 0.051 and 0.056, the maximum residual error(MaxE) was less than 0.25, and the average running time was 17.2 s and 17.6 s, respectively. [Conclusion] Compared with other models, the prediction accuracy, generalization ability and prediction efficiency of SSA-DELM model were better than other models, and the prediction error was very low, and it could satisfy the two different tillage conditions under the soil temperature prediction needs, has good stability and anti-interference ability, and can provide certain data support for agricultural production decision-making.

Keywords

soil temperature / conservation tillage / neural network / sparrow search algorithm / deep learning / numerical simulation / groundwater / black soil