Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India
https://orcid.org/http://orcid.org/0000-0002-6607-2707
Severe rainfall has seriously threatened human health and survival. Natural catastrophes such as floods, droughts, and many other natural disasters are caused by heavy rains, which people worldwide have to deal with throughout the year. Accurate rainfall forecasting is essential in nations like India where cultivation is the main occupation. Machine learning (ML) techniques are more effective than many other strategies because of the unpredictable nature of rainfall. Ensemble learning (EL) methods yield higher accuracy than ML specific classifiers. Therefore, this paper proposes an EL-based prediction model for not only a specific state of India but for the entire eastern India region, which includes ten states: West Bengal, Jharkhand, Bihar, Odisha, Assam, Tripura, Mizoram, Manipur, Nagaland and Meghalaya. We acquired the rainfall data for eastern India using NASA’s power data access view for 22 years (2000–2021). In this model, apart from classification feature selection is done using Pearson correlation and ANOVA. This study looks at the accuracy of several different classifiers, including DT, LR, and SVM. The acceptable accuracy of 96.53% for rainfall prediction in eastern India was determined using an ensemble model that includes the XGBoost ensemble technique and the classifiers DT, LR, and SVM. The findings of our experiment reveal that EL-based prediction model outperforms the individual classifiers utilized in the experiment.
Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India
https://orcid.org/http://orcid.org/0000-0002-6607-2707
Severe rainfall has seriously threatened human health and survival. Natural catastrophes such as floods, droughts, and many other natural disasters are caused by heavy rains, which people worldwide have to deal with throughout the year. Accurate rainfall forecasting is essential in nations like India where cultivation is the main occupation. Machine learning (ML) techniques are more effective than many other strategies because of the unpredictable nature of rainfall. Ensemble learning (EL) methods yield higher accuracy than ML specific classifiers. Therefore, this paper proposes an EL-based prediction model for not only a specific state of India but for the entire eastern India region, which includes ten states: West Bengal, Jharkhand, Bihar, Odisha, Assam, Tripura, Mizoram, Manipur, Nagaland and Meghalaya. We acquired the rainfall data for eastern India using NASA’s power data access view for 22 years (2000–2021). In this model, apart from classification feature selection is done using Pearson correlation and ANOVA. This study looks at the accuracy of several different classifiers, including DT, LR, and SVM. The acceptable accuracy of 96.53% for rainfall prediction in eastern India was determined using an ensemble model that includes the XGBoost ensemble technique and the classifiers DT, LR, and SVM. The findings of our experiment reveal that EL-based prediction model outperforms the individual classifiers utilized in the experiment.
Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India
https://orcid.org/http://orcid.org/0000-0002-6607-2707
Severe rainfall has seriously threatened human health and survival. Natural catastrophes such as floods, droughts, and many other natural disasters are caused by heavy rains, which people worldwide have to deal with throughout the year. Accurate rainfall forecasting is essential in nations like India where cultivation is the main occupation. Machine learning (ML) techniques are more effective than many other strategies because of the unpredictable nature of rainfall. Ensemble learning (EL) methods yield higher accuracy than ML specific classifiers. Therefore, this paper proposes an EL-based prediction model for not only a specific state of India but for the entire eastern India region, which includes ten states: West Bengal, Jharkhand, Bihar, Odisha, Assam, Tripura, Mizoram, Manipur, Nagaland and Meghalaya. We acquired the rainfall data for eastern India using NASA’s power data access view for 22 years (2000–2021). In this model, apart from classification feature selection is done using Pearson correlation and ANOVA. This study looks at the accuracy of several different classifiers, including DT, LR, and SVM. The acceptable accuracy of 96.53% for rainfall prediction in eastern India was determined using an ensemble model that includes the XGBoost ensemble technique and the classifiers DT, LR, and SVM. The findings of our experiment reveal that EL-based prediction model outperforms the individual classifiers utilized in the experiment.
Efficacy of Ensemble Learning Method over Individual Classifier for Rainfall Forecasting in Eastern India
J. Inst. Eng. India Ser. B
Karmakar, Rahul (Autor:in) / Kundu, Saranagta (Autor:in) / Biswas, Saroj Kumar (Autor:in) / Tripathi, Deeksha (Autor:in)
Journal of The Institution of Engineers (India): Series B ; 105 ; 929-939
01.08.2024
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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