Prediction of agricultural emissions in Malaysia using the arima, LSTM, and regression models
Agriculture has always been an important economical factor for a country, which is causing emissions every day, without realizing how much it is leading towards an increasing number of Greenhouse Gas (GHG). Agricultural emissions have been forecasted for Malaysia to have a better understanding and t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IIUM Press
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/91249/7/91249_Prediction%20of%20agricultural%20emissions%20in%20Malaysia.pdf http://irep.iium.edu.my/91249/ https://journals.iium.edu.my/kict/index.php/IJPCC/article/download/212/137/1344 |
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| Summary: | Agriculture has always been an important economical factor for a country, which is causing emissions every day, without realizing how much it is leading towards an increasing number of Greenhouse Gas (GHG). Agricultural emissions have been forecasted for Malaysia to have a better understanding and to take measures right away. This can be done through a machine learning model including collecting data, pre- processing, training, building a model, and testing the model for accuracy. This project aims to develop a model to forecast agricultural emissions using the three most accurate forecasting models. The time series analysis consists of two models, autoregressive integrated moving average(ARIMA) and long short-term memory(LSTM) and simple linear regression model. These models illustrate the forecasted upward trend values until 2040 in Malaysia. The ARIMA model provides good prediction curves which are close to the actual values taken since 1960 and the LSTM model provides a decreasing curve for every value loss epochs which concludes to be a good forecasting model. It was concluded that agricultural emission is causing the soaring temperature in Malaysia and an immense amount of emissions causing by agriculture. The techniques used in this paper can be enhanced more in the future and the visualizations can help the Malaysian agricultural sectors to take proper measurements to prevent this uprising agricultural emissions |
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