Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models
Section snippets
INTRODUCTION
Soil temperature (ST) plays a key role in different ecosystems, by affecting processes such as hydrological response of the soil, accumulation and degradation of organic matter, plant growth, nutrient mineralization, carbon emissions, proper time of sowing, and even micro-organism activity (Brar et al., 1992, Peng et al., 2009; Hu et al., 2016; Citakoglu, 2017). The variation of ST can alter soil characteristics and accordingly has considerable environmental outcomes with the change in carbon
Study area
Isfahan Province is located in Central Iran. It is characterized by arid and semi-arid conditions and comprises 10% of Iranian deserts. The western parts of the province encompass mountains with a mild climate. The air temperature ranges between 40.6 and 10.6 °C, the mean annual temperature is about 16.7 °C, and the mean annual rainfall is only 117 mm. Isfahan Province lies between 30° 42ʹ to 34° 30ʹ N and 49° 36ʹ to 55° E and covers an area of about 107 027 km2 (Fig. 1), of which about 5 674
Machine learning model
Instance-based K-nearest learning (IBK) is a lazy algorithm based on the K-nearest neighbor (KNN) classifier, using the Euclidean metric as a function for measuring the distance between instances (Smusz et al., 2013). Over the years, several applications of the IBK algorithm can be found in literature, for example, classification of bioactive compounds (Smusz et al., 2013), imbalance handling methods using public binary imbalanced datasets (Zhang et al., 2019), classification of foot drop gait (
Most effective input variables
It was observed that the correlation between air temperature and ST (Table II) was more important than other variables such as Eva, SSH, and SR. However, the correlation between soil and air temperatures decreased with increasing soil depth, in agreement with previous studies (Salamene et al., 2010). The influences of Eva, SSH, and SR also decreased in the deeper layers. The r values between the input variables and output revealed that Tmean was the most effective input variable for forecasting
DISCUSSION
Apart from some agronomic variables like seed availability, equipment readiness, field dryness, and tillage capacity, the timing of field crop planting is partially dependent on optimal soil temperatures for seed germination and seedling emergence (Lindstrom et al., 1976). As direct soil measurement is time-consuming and empirical models are not sufficiently accurate, the results of this study provide the bases for ST forecasting at different depths (5 and 50 cm).
The different characteristics
CONCLUSIONS
This study successfully addressed the challenge of accurately forecasting ST at two different soil depths (5 and 50 cm) using six novel hybrid models, i.e., BA-IBK, BA-KStar, BA-LWL, DA-IBK, DA-KStar, and DA-LWL. The main objectives were to develop models with reasonable prediction power and to propose them as simple and promising tools for multi-step (up to 9 d ahead) ST forecasting using Isfahan Province, Iran as a case study. The modeling process, based on r values, showed that Tmean was the
ACKNOWLEDGEMENTS
The authors would like to thank Dr. Esmaeel Dodangeh for sharing the dataset. We also would like to extend our gratitude to two anonymous reviewers for their help in enhancing the quality of the paper during the review process.
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