A review on rainfall forecasting using ensemble learning techniques
Significant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be accurate in countries like India where agricu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123001912 |
| _version_ | 1797388635849359360 |
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| author | Saranagata Kundu Saroj Kr. Biswas Deeksha Tripathi Rahul Karmakar Sounak Majumdar Sudipta Mandal |
| author_facet | Saranagata Kundu Saroj Kr. Biswas Deeksha Tripathi Rahul Karmakar Sounak Majumdar Sudipta Mandal |
| author_sort | Saranagata Kundu |
| collection | DOAJ |
| description | Significant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be accurate in countries like India where agriculture is the primary occupation. The non-linearity of rainfall makes machine learning (ML) methods more efficient than many other approaches. In machine learning (ML), individual classifiers are less accurate than ensemble learning (EL) techniques. In order to better understand the various Machine Learning algorithms and Ensemble Learning techniques that researchers employ to predict rainfall, this review paper has been written.This article reviews ensemble learning algorithms for predicting rainfall. In order to increase the accuracy of rainfall forecasts and consequently avoid the negative effects of heavy precipitation, ensemble learning algorithms have gained popularity. This review article examines and makes reference to the development of ensemble approaches, including bagging, boosting, and stacking. The findings of this survey demonstrate that ensemble techniques are much superior to conventional (individual) model learning in terms of rainfall prediction. Additionally, boosting techniques (such enabling, AdaBoost, and extreme gradient boosting) have been applied more frequently and successfully in scenarios involving rainfall forecasting. |
| first_indexed | 2024-03-08T22:43:39Z |
| format | Article |
| id | doaj.art-cafa8fe4aebb469b9198f56afec33473 |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-08T22:43:39Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-cafa8fe4aebb469b9198f56afec334732023-12-17T06:43:17ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-12-016100296A review on rainfall forecasting using ensemble learning techniquesSaranagata Kundu0Saroj Kr. Biswas1Deeksha Tripathi2Rahul Karmakar3Sounak Majumdar4Sudipta Mandal5Department of Computer Science, The University of Burdwan, Bardhaman, IndiaDepartment of Computer Science & Engineering, National Institute of Technology, Silchar, IndiaDepartment of Computer Science & Engineering, National Institute of Technology, Silchar, IndiaDepartment of Computer Science, The University of Burdwan, Bardhaman, India; Corresponding author.Department of Computer Science & Engineering, National Institute of Technology, Silchar, IndiaDepartment of Computer Science & Engineering, National Institute of Technology, Silchar, IndiaSignificant challenges to human health and life have arisen as a result of heavy rains. Floods and other natural disasters that affect people all over the world every year are caused by prolonged periods of heavy rainfall. Predictions of rainfall must be accurate in countries like India where agriculture is the primary occupation. The non-linearity of rainfall makes machine learning (ML) methods more efficient than many other approaches. In machine learning (ML), individual classifiers are less accurate than ensemble learning (EL) techniques. In order to better understand the various Machine Learning algorithms and Ensemble Learning techniques that researchers employ to predict rainfall, this review paper has been written.This article reviews ensemble learning algorithms for predicting rainfall. In order to increase the accuracy of rainfall forecasts and consequently avoid the negative effects of heavy precipitation, ensemble learning algorithms have gained popularity. This review article examines and makes reference to the development of ensemble approaches, including bagging, boosting, and stacking. The findings of this survey demonstrate that ensemble techniques are much superior to conventional (individual) model learning in terms of rainfall prediction. Additionally, boosting techniques (such enabling, AdaBoost, and extreme gradient boosting) have been applied more frequently and successfully in scenarios involving rainfall forecasting.http://www.sciencedirect.com/science/article/pii/S2772671123001912RainfallMLELMLRSVMARIMA |
| spellingShingle | Saranagata Kundu Saroj Kr. Biswas Deeksha Tripathi Rahul Karmakar Sounak Majumdar Sudipta Mandal A review on rainfall forecasting using ensemble learning techniques e-Prime: Advances in Electrical Engineering, Electronics and Energy Rainfall ML EL MLR SVM ARIMA |
| title | A review on rainfall forecasting using ensemble learning techniques |
| title_full | A review on rainfall forecasting using ensemble learning techniques |
| title_fullStr | A review on rainfall forecasting using ensemble learning techniques |
| title_full_unstemmed | A review on rainfall forecasting using ensemble learning techniques |
| title_short | A review on rainfall forecasting using ensemble learning techniques |
| title_sort | review on rainfall forecasting using ensemble learning techniques |
| topic | Rainfall ML EL MLR SVM ARIMA |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123001912 |
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