Department of Water Engineering, Urmia University, Urmia, Iran.
Department of Water Engineering, University of Tabriz, Tabriz, Iran.
Environ Sci Pollut Res Int. 2024 Oct;31(47):57903-57919. doi: 10.1007/s11356-024-35002-1. Epub 2024 Sep 20.
Soil temperature (ST) stands as a pivotal parameter in the realm of water resources and irrigation. It serves as a guide for farmers, enabling them to determine optimal planting and fertilization timings. In the backdrop of regions like Iran, where water resources are scarce, a proficient and economical prediction model for ST, particularly at lower depths, becomes imperative. While recent models have demonstrated adeptness in predicting ST, in general, their error decreases with increasing depth, so that they had the lowest error at a depth of 100 cm. Addressing this gap, our study pioneers a novel hybrid model that excels in accurate daily ST prediction as it delves deeper. The models deployed encompass the multilayer perceptron (MLP) and an enhanced version, MLP coupled with the Sperm Swarm Optimization Algorithm (MLP-SSO). These models prognosticate daily ST across varying depths (5-100 cm), leveraging meteorological parameters such as air temperature, relative humidity, wind speed, sunshine hours, and precipitation. These parameters are anchored to the Ahvaz and Sabzevar synoptic stations in Iran, spanned over the period from 1997 to 2022. Evaluation of our research outcomes unveils that the root mean square error (RMSE) witnesses its most substantial reduction at a depth of 100 cm. For instance, at the Ahvaz station, the MLP-SSO model diminishes the RMSE value from 1.25 to 1.12 °C, in contrast to the MLP model. Similarly, at the Sabzevar station, the RMSE value drops from 1.78 to 1.49 °C using the coupled MLP-SSO model. These results robustly highlight the considerable enhancement brought about by the utilization of the MLP-SSO model, clearly surpassing the performance of the standalone MLP model. This emphasizes the potential and promise of the MLP-SSO model for future investigations, offering insights that can significantly advance the domain of soil temperature prediction and its implications for agricultural decision-making.
土壤温度（ST）是水资源和灌溉领域的关键参数。它为农民提供指导，使他们能够确定最佳的种植和施肥时间。在伊朗等水资源稀缺的地区，建立一个精确且经济的 ST 预测模型，特别是在较浅的深度下，变得至关重要。尽管最近的模型在一般情况下表现出了预测 ST 的能力，但它们的误差随着深度的增加而减小，因此在深度为 100cm 时误差最小。为了解决这一差距，我们的研究提出了一种新颖的混合模型，该模型在深入研究时擅长准确预测每日 ST。所使用的模型包括多层感知器（MLP）和增强版本，即与精子群优化算法（MLP-SSO）相结合的 MLP（MLP-SSO）。这些模型利用气象参数（如空气温度、相对湿度、风速、日照小时数和降水量）预测不同深度（5-100cm）的每日 ST。这些参数基于伊朗的 Ahvaz 和 Sabzevar 天气站，时间跨度为 1997 年至 2022 年。我们研究结果的评估表明，在 100cm 的深度下，均方根误差（RMSE）的降幅最大。例如，在 Ahvaz 站，MLP-SSO 模型将 RMSE 值从 1.25°C 降低到 1.12°C，而 MLP 模型则从 1.78°C 降低到 1.49°C。同样，在 Sabzevar 站，使用耦合的 MLP-SSO 模型，RMSE 值从 1.78°C 降低到 1.49°C。这些结果有力地突出了 MLP-SSO 模型的显著增强，明显优于独立的 MLP 模型的性能。这强调了 MLP-SSO 模型在未来研究中的潜力和前景，为土壤温度预测及其对农业决策的影响提供了有价值的见解。
