Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India
This research paper delves into creating and comparing rainfall prediction models, employing diverse machine learning algorithms, including Logistic Regression, Decision Tree Classifier, Multi-Layer Perceptron classifier (neural network), and Random Forest. The study aims not only to predict rainfal...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024003475 |
| _version_ | 1827215356485173248 |
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| author | Mohd Usman Saeed Khan Khan Mohammad Saifullah Ajmal Hussain Hazi Mohammad Azamathulla |
| author_facet | Mohd Usman Saeed Khan Khan Mohammad Saifullah Ajmal Hussain Hazi Mohammad Azamathulla |
| author_sort | Mohd Usman Saeed Khan |
| collection | DOAJ |
| description | This research paper delves into creating and comparing rainfall prediction models, employing diverse machine learning algorithms, including Logistic Regression, Decision Tree Classifier, Multi-Layer Perceptron classifier (neural network), and Random Forest. The study aims not only to predict rainfall patterns but also to evaluate the performance of each model through metrics such as Accuracy, Cohen's kappa coefficient, and Receiver Operating Characteristic (ROC) curve analysis. Additionally, the relevance of the predictors employed in each model is thoroughly assessed. The results of extensive experimentation and analysis reveal that the Logistic Regression (Accuracy = 82.80 %, ROC = 82.45 %, Cohen's Kappa = 65.05 %) and Neural Network model (Accuracy = 82.59 %, ROC = 81.94 %, Cohen's Kappa = 64.40 %) has emerged as the most promising approach, achieving the highest percentage of accuracy, ROC and Cohen's Kappa metrics; among the models considered. This outcome underscores the effectiveness of Logistic Regression and Neural Network architectures in capturing intricate patterns and relationships within rainfall data. |
| first_indexed | 2024-04-24T10:03:49Z |
| format | Article |
| id | doaj.art-1845ca49dcfb4cd8bbcb9ed241b1c8c6 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2025-03-21T14:45:56Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-1845ca49dcfb4cd8bbcb9ed241b1c8c62024-06-21T08:54:36ZengElsevierResults in Engineering2590-12302024-06-0122102093Comparative analysis of different rainfall prediction models: A case study of Aligarh City, IndiaMohd Usman Saeed Khan0Khan Mohammad Saifullah1Ajmal Hussain2Hazi Mohammad Azamathulla3Department of Civil Engineering, Zakir Hussain College of Engineering & Technology, Aligarh Muslim University, Aligarh, 202002, IndiaDepartment of Industrial Chemistry, Faculty of Science, Aligarh Muslim University, Aligarh, 202002, IndiaDepartment of Civil Engineering, Zakir Hussain College of Engineering & Technology, Aligarh Muslim University, Aligarh, 202002, IndiaDepartment of Civil and Environmental Engineering, University of the West Indies, Saint Augustine, Trinidad and Tobago; Corresponding author.This research paper delves into creating and comparing rainfall prediction models, employing diverse machine learning algorithms, including Logistic Regression, Decision Tree Classifier, Multi-Layer Perceptron classifier (neural network), and Random Forest. The study aims not only to predict rainfall patterns but also to evaluate the performance of each model through metrics such as Accuracy, Cohen's kappa coefficient, and Receiver Operating Characteristic (ROC) curve analysis. Additionally, the relevance of the predictors employed in each model is thoroughly assessed. The results of extensive experimentation and analysis reveal that the Logistic Regression (Accuracy = 82.80 %, ROC = 82.45 %, Cohen's Kappa = 65.05 %) and Neural Network model (Accuracy = 82.59 %, ROC = 81.94 %, Cohen's Kappa = 64.40 %) has emerged as the most promising approach, achieving the highest percentage of accuracy, ROC and Cohen's Kappa metrics; among the models considered. This outcome underscores the effectiveness of Logistic Regression and Neural Network architectures in capturing intricate patterns and relationships within rainfall data.http://www.sciencedirect.com/science/article/pii/S2590123024003475Rainfall predictionDeep learningMachine learningWater resources |
| spellingShingle | Mohd Usman Saeed Khan Khan Mohammad Saifullah Ajmal Hussain Hazi Mohammad Azamathulla Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India Results in Engineering Rainfall prediction Deep learning Machine learning Water resources |
| title | Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India |
| title_full | Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India |
| title_fullStr | Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India |
| title_full_unstemmed | Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India |
| title_short | Comparative analysis of different rainfall prediction models: A case study of Aligarh City, India |
| title_sort | comparative analysis of different rainfall prediction models a case study of aligarh city india |
| topic | Rainfall prediction Deep learning Machine learning Water resources |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024003475 |
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