Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India
Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4572 |
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| author | Asha Jayasree Santhosh Kumar Sasidharan Rishidas Sivadas Jayan A. Ramakrishnan |
| author_facet | Asha Jayasree Santhosh Kumar Sasidharan Rishidas Sivadas Jayan A. Ramakrishnan |
| author_sort | Asha Jayasree |
| collection | DOAJ |
| description | Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as floods and landslides. In this paper, a novel hybrid model of empirical mode decomposition (EMD) and random forest (RF) was developed to enhance the accuracy of annual rainfall prediction. The EMD technique was utilized to decompose the rainfall signal into six intrinsic mode functions (IMFs) to extract underlying patterns, while the RF algorithm was employed to make predictions based on the IMFs. The hybrid RF–IMF model was trained and tested using a dataset of annual rainfall in Kerala from 1871 to 2020, and its performance was compared to traditional models such as RF regression and the autoregressive moving average (ARMA) model. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R<sup>2</sup>) were used to compare the performances of these three models. Model evaluation metrics show that the RF–IMF model outperformed both the RF model and ARMA model. |
| first_indexed | 2024-03-11T05:41:46Z |
| format | Article |
| id | doaj.art-4b517dd7fccb446284fdf07f0295f012 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:41:46Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4b517dd7fccb446284fdf07f0295f0122023-11-17T16:22:16ZengMDPI AGApplied Sciences2076-34172023-04-01137457210.3390/app13074572Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, IndiaAsha Jayasree0Santhosh Kumar Sasidharan1Rishidas Sivadas2Jayan A. Ramakrishnan3Department of Electronics and Communication Engineering, Government Engineering College Thrissur, APJ Abdul Kalam Technological University, Thrissur 680009, IndiaDepartment of Electronics and Communication Engineering, Government Engineering College Idukki, APJ Abdul Kalam Technological University, Painavu 685603, IndiaDepartment of Electronics and Communication Engineering, Government Engineering College Idukki, APJ Abdul Kalam Technological University, Painavu 685603, IndiaDepartment of Electronics and Communication Engineering, Government Engineering College Thrissur, APJ Abdul Kalam Technological University, Thrissur 680009, IndiaRainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as floods and landslides. In this paper, a novel hybrid model of empirical mode decomposition (EMD) and random forest (RF) was developed to enhance the accuracy of annual rainfall prediction. The EMD technique was utilized to decompose the rainfall signal into six intrinsic mode functions (IMFs) to extract underlying patterns, while the RF algorithm was employed to make predictions based on the IMFs. The hybrid RF–IMF model was trained and tested using a dataset of annual rainfall in Kerala from 1871 to 2020, and its performance was compared to traditional models such as RF regression and the autoregressive moving average (ARMA) model. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R<sup>2</sup>) were used to compare the performances of these three models. Model evaluation metrics show that the RF–IMF model outperformed both the RF model and ARMA model.https://www.mdpi.com/2076-3417/13/7/4572annual rainfallempirical mode decompositionintrinsic mode functionsrandom forest regressionautoregressive moving averagePearson correlation |
| spellingShingle | Asha Jayasree Santhosh Kumar Sasidharan Rishidas Sivadas Jayan A. Ramakrishnan Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India Applied Sciences annual rainfall empirical mode decomposition intrinsic mode functions random forest regression autoregressive moving average Pearson correlation |
| title | Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India |
| title_full | Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India |
| title_fullStr | Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India |
| title_full_unstemmed | Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India |
| title_short | Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India |
| title_sort | hybrid emd rf model for predicting annual rainfall in kerala india |
| topic | annual rainfall empirical mode decomposition intrinsic mode functions random forest regression autoregressive moving average Pearson correlation |
| url | https://www.mdpi.com/2076-3417/13/7/4572 |
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