Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe

Gos, Magdalena; Krzyszczak, Jaromir; Baranowski, Piotr; Murat, Małgorzata; Malinowska, Iwona

doi:10.1016/j.agrformet.2019.107827

Cited by 18 publications

(10 citation statements)

References 51 publications

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“…To select the best prediction model (with the optimal combination of input variables), the Akaike information criterion (AIC) was applied [38][39][40]. The AIC is one of the most widespread tools in statistical modelling.…”

Section: Quality Metricsmentioning

confidence: 99%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

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An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

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Section: Quality Metricsmentioning

confidence: 99%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

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“…The prediction in the time series of the daily maximum, average, and minimum temperature is carried out by Ustaoglu et al [10] in Geyve and Sakarya, Marmara region, Turkey, using three ANN models for each temperature and looking for the architecture for the best fit; from the data of the seven days prior to the prediction, the ANN configurations obtained the best RMSE settings for temperature (maximum, average, and minimum) in Geyve (3.49, 2.08, 2.47) • C and in Sakarya (3.75, 2.31, 2.48) • C. These results show that using the appropriate predictive variables achieves better results than searching for the best ANN configuration. Gos et al [11] estimate the time series of daily maximum and minimum air temperatures over a six-year period for various climatic locations in Europe using a combination of models. Ju-Young et al [12] propose a hybrid model for the global climate model using machine learning to generate seasonal forecasts, up to 90 days, of the average daily air temperature (RMSE, 1.02-3.35).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain

Díez

Corrêa-Guimarães

Chico-Santamarta

et al. 2022

Sensors

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This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.

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“…To include the effect of climate change in GDUs, we predicted GDUs through time series analysis of daily historical data. For time series analysis and prediction, TBATS, which stands for Trigonometric Exponential Smoothing with Box-Cox transformation, ARMA errors, Trend, and Seasonal decomposition, has been widely adopted in research ( Shin and Yoon, 2016 ; Cherrie et al, 2018 ; Naim et al, 2018 ; Gos et al, 2020 ; Gonçalves et al, 2021 ). In our study, TBATS has been chosen as a benchmark prediction model.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Crop Planting Schedule Considering Planting Window and Storage Capacity

Sajid

2022

Front. Plant Sci.

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Technology advancement has contributed significantly to productivity improvement in the agricultural sector. However, field operation and farm resource utilization remain a challenge. For major row crops, designing an optimal crop planting strategy is crucial since the planting dates are contingent upon weather conditions and storage capacity. This manuscript proposes a two-stage decision support system to optimize planting decisions, considering weather uncertainties and resource constraints. The first stage involves creating a weather prediction model for Growing Degree Units (GDUs). In the second stage, the GDUs prediction from the first stage is incorporated to formulate an optimization model for the planting schedule. The efficacy of the proposed model is demonstrated through a case study based on Syngenta Crop Challenge (2021). It has been shown that the 1D-CNN model outperforms other prediction models with an RRMSE of 7 to 8% for two different locations. The decision-making model in the second stage provides an optimal planting schedule such that weekly harvested quantities will be evenly allocated utilizing a minimum number of harvesting weeks. We analyzed the model performance for two scenarios: fixed and flexible storage capacity at multiple geographic locations. Results suggest that the proposed model can provide an optimized planting schedule considering planting window and storage capacity. The model has also demonstrated its robustness under multiple scenarios.

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Combined TBATS and SVM model of minimum and maximum air temperatures applied to wheat yield prediction at different locations in Europe

Cited by 18 publications

References 51 publications

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Prediction of Daily Ambient Temperature and Its Hourly Estimation Using Artificial Neural Networks in an Agrometeorological Station in Castile and León, Spain

Optimizing Crop Planting Schedule Considering Planting Window and Storage Capacity

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