Improved ARIMAX modal based on ANN and SVM approaches for forecasting rice yield using weather variables

Alam, Wasi; Ray, Mrinmoy; Kumar, Rajeev Ranjan; Sinha, Kishore; Rathod, Santosha; Singh, K. N.

doi:10.56093/ijas.v88i12.85446

Cited by 17 publications

(15 citation statements)

References 6 publications

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“…The results of the DM test revealed that in two sets (training and testing set) of data, the extreme learning machine intervention model performed better than all other models (Table 8). According to several studies [38][39][40][41][42][43][44] for forecasting time series data in the agricul-tural and related fields, the results showed that AI performed better than the standard ARIMA model, which is in accordance with some previous findings. By considering the MAPE values, a significant difference between the actual and forecasted values can be obtained by the DM test.…”

Section: Discussionsupporting

confidence: 87%

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

et al. 2022

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Via national lockdowns, the COVID-19 pandemic disrupted the production and distribution of foodstuffs worldwide, including rice (Oryza sativa L.) production, affecting the prices in India’s agroecosystems and markets. The present study was performed to assess the impact of the COVID-19 national lockdown on rice prices in India, and to develop statistical machine learning models to forecast price changes under similar crisis scenarios. To estimate the rice prices under COVID-19, the general time series models, such as the autoregressive integrated moving average (ARIMA) model, the artificial neural network (ANN) model, and the extreme learning machine (ELM) model, were applied. The results obtained using the ARIMA intervention model revealed that during the COVID-19 lockdown in India, rice prices increased by INR 0.92/kg. In addition, the ELM intervention model was faster, with less computation time, and provided better results vs other models because it detects the nonlinear time series data, along with the intervention variable, which was considered an exogenous variable. The use of forecasting models can be a useful tool in supporting decision makers, especially under unpredictable crises. The study results are of great importance for the national agri-food sector, as they can bolster authorities and policymakers in planning and designing more sustainable interventions in the food market during (inter)national crisis situations.

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Section: Discussionsupporting

confidence: 87%

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

et al. 2022

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“…For the ARIMAX model, d was set to the default value following the stability test. The influence of p and q on forecast accuracy is discussed later in the section [12]. Results of four groups of cases corresponding to the two case ships are shown in Figure 11.…”

Section: Optimization Of Parametersmentioning

confidence: 99%

“…For multivariate time-series forecasting, the ARIMAX model is a generalization of ARIMA model, which is capable of incorporating an exogenous input variable [12], as shown in Equation (4).…”

Section: Time-series Analysesmentioning

confidence: 99%

“…This study fills a gap in the application of BLS to time-series analyses for fuel consumption. To determine a model that demands minimal time and is more accurate with different structures and complexity, BLS was compared with autoregressive integrated moving average model with exogenous inputs (ARIMAX) [12], SVR [8], recurrent neural network (RNN), LSTM [10] and extreme learning machines (ELM) [13], which are models based on time-series analyses and machine learning techniques. According to the characteristics of different models to demonstrated the applicability and accuracy by systematic verification analysis.…”

Section: Introductionmentioning

confidence: 99%

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Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption

Zuo

Jiang

2022

Sustainability

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Accurately forecasting the fuel consumption of ships is critical for improving their energy efficiency. However, the environmental factors that affect ship fuel consumption have not been researched comprehensively, and most of the relevant studies continue to present efficiency and accuracy issues. In view of such problems, a time-series forecasting model of ship fuel consumption based on a novel regression analysis using broad learning system (BLS) was developed in this study. The BLS was compared to a diverse set of fuel consumption forecasting models based on time-series analyses and machine learning techniques, including autoregressive integrated moving average model with exogenous inputs (ARIMAX), support vector regression (SVR), recurrent neural network (RNN), long short-term memory network (LSTM), and extreme learning machines (ELM). In the experiment, two types of passenger roll-on roll-off (ro-ro) ship and liquefied petroleum gas (LPG) carrierwere used as research objects to verify the proposed method’s generalizability, with data divided among two groups (RM, RB). The experimental results showed that the BLS model is the best choice to forecast fuel consumption in actual navigation, with mean absolute error (MAE) values of 0.0140 and 0.0115 on RM and RB, respectively. For the LPG carrier, it has also been proven that the forecast effect is improved when factoring the sea condition, with MAE reaching 0.0108 and 0.0142 under ballast and laden conditions, respectively. Furthermore, the BLS features the advantages of low computing complexity and short forecast time, making it more suitable for real-world applications. The results of this study can therefore effectively improve the energy efficiency of ships by reducing operating costs and emissions.

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“…In order to forecast rice yield, Kumar and Kumar (2012) added fuzzy values to the time series [12]. Alam et al (2018) applied two hybrid approaches including ARIMAX-ANN and ARIMAX-SVM for estimating rice yield in India [13]. Jing-feng (2011) used NOAA/AVHRR data to predict rice production in Zhejiang Province through ratio models and regression models [14].…”

Section: Introductionmentioning

confidence: 99%

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

2023

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In this study, we attempt to anticipate annual rice production in Bangladesh (1961–2020) using both the Autoregressive Integrated Moving Average (ARIMA) and the eXtreme Gradient Boosting (XGBoost) methods and compare their respective performances. On the basis of the lowest Corrected Akaike Information Criteria (AICc) values, a significant ARIMA (0, 1, 1) model with drift was chosen based on the findings. The drift parameter value shows that the production of rice positively trends upward. Thus, the ARIMA (0, 1, 1) model with drift was found to be significant. On the other hand, the XGBoost model for time series data was developed by changing the tunning parameters frequently with the greatest result. The four prominent error measures, such as mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE), and mean absolute percentage error (MAPE), were used to assess the predictive performance of each model. We found that the error measures of the XGBoost model in the test set were comparatively lower than those of the ARIMA model. Comparatively, the MAPE value of the test set of the XGBoost model (5.38%) was lower than that of the ARIMA model (7.23%), indicating that XGBoost performs better than ARIMA at predicting the annual rice production in Bangladesh. Hence, the XGBoost model performs better than the ARIMA model in predicting the annual rice production in Bangladesh. Therefore, based on the better performance, the study forecasted the annual rice production for the next 10 years using the XGBoost model. According to our predictions, the annual rice production in Bangladesh will vary from 57,850,318 tons in 2021 to 82,256,944 tons in 2030. The forecast indicated that the amount of rice produced annually in Bangladesh will increase in the years to come.

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Improved ARIMAX modal based on ANN and SVM approaches for forecasting rice yield using weather variables

Cited by 17 publications

References 6 publications

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption

A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh

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