Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

Araghi, Alireza; Baygi, Mohammad Mousavi; Adamowski, Jan; Martinez, Christopher J.; Ploeg, Martine van der

doi:10.1002/met.1661

Cited by 33 publications

(19 citation statements)

References 24 publications

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“…Temperature (i.e., water temperature, soil temperature and Urban Heat Island, etc.) forecasting plays an important role in eco-environment-related research involving the functioning of the eco-environment system [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. In industry, accurate forecasts of temperature are part of an energy-management strategy to reduce energy consumption while maintaining an internal temperature within a specified comfort range [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

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Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

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Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Zhang

Gui

et al. 2018

IJERPH

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“…Soil temperature acts as important roles in physical, chemical, and biological processes. It is relevant to soil science, agriculture, hydrology, meteorology, environmental science and many other research fields [1,2]. Soil temperature affects the balance of heat energy between the atmosphere and the land surface [3].…”

Section: Introductionmentioning

confidence: 99%

Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Hao

2021

IEEE Access

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“…Frequent extreme climate events such as spring freezes and summer heat stress can impact winter wheat [Triticum aestivum L.] growth and development, reducing grain yields by more than 7% in the U.S. winter wheat belt (Tack et al, 2015;Paulsen and Heyne, 1983). These effects are also modulated through land-surface interaction processes (Hillel, 1998;Araghi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…CC BY 4.0 License. nutrient uptake (Yan et al, 2012), and estimate of crop production (Araghi et al, 2017;Hillel, 1998).…”

Section: Introductionmentioning

confidence: 99%

Daily soil temperature modeling improved by integrating observed snow cover and estimated soil moisture in the U.S. Great Plains

Zhao¹,

Sassenrath²,

Kirkham³

et al. 2021

Preprint

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Abstract. Soil temperature data are vital for explaining many biological, physical, and hydrological process in soil surface layer and in near surface atmosphere. However, soil temperature data are limited in the U.S. Great Plains. In this study we developed a model for predicting daily soil temperature at the 10 cm depth across Nebraska, Kansas, Oklahoma, and parts of Texas. This improved model was developed and tested by using 87 weather stations. Our results indicated that our improved model, on average, is able to predict daily soil temperature 0.6 oC better than original methods for all stations in the U.S. Great Plains. This improved model can provide us a daily soil temperature modeling tool that overcomes the deficiencies of soil temperature data used in applications for climatic changes, hydrological modeling, and winter wheat production in the U.S. Great Plains.

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Forecasting soil temperature based on surface air temperature using a wavelet artificial neural network

Cited by 33 publications

References 24 publications

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Daily soil temperature modeling improved by integrating observed snow cover and estimated soil moisture in the U.S. Great Plains

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