ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh

Md. Habibur Rahman

doi:doi:10.11648/j.ijdsa.20241006.11

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ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh

Md. Habibur Rahman^*

Published in International Journal of Data Science and Analysis (Volume 10, Issue 6)

Received: 29 October 2024 Accepted: 10 December 2024 Published: 18 December 2024

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Abstract

The study area defined by the coordinates (90°E − 92°E, 23°N − 25°N) is a significant region in Bangladesh, where accurate rainfall predictions are crucial for both the local population and policymakers. Understanding rainfall patterns in this area is vital for effective planning and resource management. Data on atmospheric variables, including temperature, rainfall, humidity, sea level pressure, and wind speed were collected from the Bangladesh Meteorological Department for various locations across the study grids for the period of 1964 to 2015. The descriptive statistics revealed that the pattern of the data of climate parameters is not normal. This dataset serves as the foundation for analyzing climate parameters and forecasting rainfall levels within the specified regions of Bangladesh. This study evaluates machine learning techniques, focusing on artificial neural networks (ANN) and classification and regression trees, C5.0, Random Forest, and Gradient Boosting as alternatives to traditional statistical models for predicting atmospheric phenomena. It reveals that conventional models often rely on assumptions unsuitable for chaotic systems like the atmosphere. Among the assessed models ANN, CART, C5.0, Random Forest (RF), and Gradient Boosting Machines (GBM) the ANN demonstrated the highest predictive capabilities for rainfall forecasting in Bangladesh, achieving superior training accuracy and Kappa values while also being recognized as the best overall performer based on ranking metrics.

Published in	International Journal of Data Science and Analysis (Volume 10, Issue 6)
DOI	10.11648/j.ijdsa.20241006.11
Page(s)	109-128
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

ANN, Decision Tree, GBM, Cross-validation, Rainfall, Bangladesh

References

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Rahman, M. H. (2024). ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. International Journal of Data Science and Analysis, 10(6), 109-128. https://doi.org/10.11648/j.ijdsa.20241006.11

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ACS Style

Rahman, M. H. ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. Int. J. Data Sci. Anal. 2024, 10(6), 109-128. doi: 10.11648/j.ijdsa.20241006.11

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AMA Style

Rahman MH. ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. Int J Data Sci Anal. 2024;10(6):109-128. doi: 10.11648/j.ijdsa.20241006.11

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@article{10.11648/j.ijdsa.20241006.11,
  author = {Md. Habibur Rahman},
  title = {ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {6},
  pages = {109-128},
  doi = {10.11648/j.ijdsa.20241006.11},
  url = {https://doi.org/10.11648/j.ijdsa.20241006.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241006.11},
  abstract = {The study area defined by the coordinates (90°E − 92°E, 23°N − 25°N) is a significant region in Bangladesh, where accurate rainfall predictions are crucial for both the local population and policymakers. Understanding rainfall patterns in this area is vital for effective planning and resource management. Data on atmospheric variables, including temperature, rainfall, humidity, sea level pressure, and wind speed were collected from the Bangladesh Meteorological Department for various locations across the study grids for the period of 1964 to 2015. The descriptive statistics revealed that the pattern of the data of climate parameters is not normal. This dataset serves as the foundation for analyzing climate parameters and forecasting rainfall levels within the specified regions of Bangladesh. This study evaluates machine learning techniques, focusing on artificial neural networks (ANN) and classification and regression trees, C5.0, Random Forest, and Gradient Boosting as alternatives to traditional statistical models for predicting atmospheric phenomena. It reveals that conventional models often rely on assumptions unsuitable for chaotic systems like the atmosphere. Among the assessed models ANN, CART, C5.0, Random Forest (RF), and Gradient Boosting Machines (GBM) the ANN demonstrated the highest predictive capabilities for rainfall forecasting in Bangladesh, achieving superior training accuracy and Kappa values while also being recognized as the best overall performer based on ranking metrics.},
 year = {2024}
}

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TY  - JOUR
T1  - ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh
AU  - Md. Habibur Rahman
Y1  - 2024/12/18
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241006.11
DO  - 10.11648/j.ijdsa.20241006.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 109
EP  - 128
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241006.11
AB  - The study area defined by the coordinates (90°E − 92°E, 23°N − 25°N) is a significant region in Bangladesh, where accurate rainfall predictions are crucial for both the local population and policymakers. Understanding rainfall patterns in this area is vital for effective planning and resource management. Data on atmospheric variables, including temperature, rainfall, humidity, sea level pressure, and wind speed were collected from the Bangladesh Meteorological Department for various locations across the study grids for the period of 1964 to 2015. The descriptive statistics revealed that the pattern of the data of climate parameters is not normal. This dataset serves as the foundation for analyzing climate parameters and forecasting rainfall levels within the specified regions of Bangladesh. This study evaluates machine learning techniques, focusing on artificial neural networks (ANN) and classification and regression trees, C5.0, Random Forest, and Gradient Boosting as alternatives to traditional statistical models for predicting atmospheric phenomena. It reveals that conventional models often rely on assumptions unsuitable for chaotic systems like the atmosphere. Among the assessed models ANN, CART, C5.0, Random Forest (RF), and Gradient Boosting Machines (GBM) the ANN demonstrated the highest predictive capabilities for rainfall forecasting in Bangladesh, achieving superior training accuracy and Kappa values while also being recognized as the best overall performer based on ranking metrics.
VL  - 10
IS  - 6
ER  -

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Md. Habibur Rahman

Department of Statistics and Data Science, Jahangirnagar University, Dhaka, Bangladesh

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Rahman, M. H. (2024). ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. International Journal of Data Science and Analysis, 10(6), 109-128. https://doi.org/10.11648/j.ijdsa.20241006.11

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ACS Style

Rahman, M. H. ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. Int. J. Data Sci. Anal. 2024, 10(6), 109-128. doi: 10.11648/j.ijdsa.20241006.11

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AMA Style

Rahman MH. ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh. Int J Data Sci Anal. 2024;10(6):109-128. doi: 10.11648/j.ijdsa.20241006.11

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@article{10.11648/j.ijdsa.20241006.11,
  author = {Md. Habibur Rahman},
  title = {ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {6},
  pages = {109-128},
  doi = {10.11648/j.ijdsa.20241006.11},
  url = {https://doi.org/10.11648/j.ijdsa.20241006.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241006.11},
  abstract = {The study area defined by the coordinates (90°E − 92°E, 23°N − 25°N) is a significant region in Bangladesh, where accurate rainfall predictions are crucial for both the local population and policymakers. Understanding rainfall patterns in this area is vital for effective planning and resource management. Data on atmospheric variables, including temperature, rainfall, humidity, sea level pressure, and wind speed were collected from the Bangladesh Meteorological Department for various locations across the study grids for the period of 1964 to 2015. The descriptive statistics revealed that the pattern of the data of climate parameters is not normal. This dataset serves as the foundation for analyzing climate parameters and forecasting rainfall levels within the specified regions of Bangladesh. This study evaluates machine learning techniques, focusing on artificial neural networks (ANN) and classification and regression trees, C5.0, Random Forest, and Gradient Boosting as alternatives to traditional statistical models for predicting atmospheric phenomena. It reveals that conventional models often rely on assumptions unsuitable for chaotic systems like the atmosphere. Among the assessed models ANN, CART, C5.0, Random Forest (RF), and Gradient Boosting Machines (GBM) the ANN demonstrated the highest predictive capabilities for rainfall forecasting in Bangladesh, achieving superior training accuracy and Kappa values while also being recognized as the best overall performer based on ranking metrics.},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - ANN-based and DT-based Classification Approaches to Predict the Rainfall Level of the Grid (90°E − 92°E, 23°N − 25°N) in Bangladesh
AU  - Md. Habibur Rahman
Y1  - 2024/12/18
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241006.11
DO  - 10.11648/j.ijdsa.20241006.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 109
EP  - 128
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241006.11
AB  - The study area defined by the coordinates (90°E − 92°E, 23°N − 25°N) is a significant region in Bangladesh, where accurate rainfall predictions are crucial for both the local population and policymakers. Understanding rainfall patterns in this area is vital for effective planning and resource management. Data on atmospheric variables, including temperature, rainfall, humidity, sea level pressure, and wind speed were collected from the Bangladesh Meteorological Department for various locations across the study grids for the period of 1964 to 2015. The descriptive statistics revealed that the pattern of the data of climate parameters is not normal. This dataset serves as the foundation for analyzing climate parameters and forecasting rainfall levels within the specified regions of Bangladesh. This study evaluates machine learning techniques, focusing on artificial neural networks (ANN) and classification and regression trees, C5.0, Random Forest, and Gradient Boosting as alternatives to traditional statistical models for predicting atmospheric phenomena. It reveals that conventional models often rely on assumptions unsuitable for chaotic systems like the atmosphere. Among the assessed models ANN, CART, C5.0, Random Forest (RF), and Gradient Boosting Machines (GBM) the ANN demonstrated the highest predictive capabilities for rainfall forecasting in Bangladesh, achieving superior training accuracy and Kappa values while also being recognized as the best overall performer based on ranking metrics.
VL  - 10
IS  - 6
ER  -

Copy | Download