Standardization Of Rainfall Prediction In Bangladesh Using Machine Learning Approach

Ria, Nushrat Jahan; Ani, Jannatul Ferdous; Islam, Saiful; Masum, Abu Kaisar Mohammad

doi:10.1109/icccnt51525.2021.9579472

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…The findings suggest the potential of neural networks in capturing the temporal dynamics of rainfall patterns and making accurate predictions over different time horizons. Ria et al [11] conducted a rain prediction study using machine learning models based on a dataset acquired from the Bangladesh Jatiyo Tottho Batayon website. They trained and evaluated five different models to predict rainfall.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness

Hassan,

Rony,

Khan

et al. 2023

IEEE Access

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Rainfall prediction plays a crucial role in raising awareness about the potential dangers associated with rain and enabling individuals to take proactive measures for their safety. This study aims to utilize machine learning algorithms to accurately predict rainfall, considering the significant impact of scarcity or extreme rainfall on both rural and urban life. The complex nature of rainfall, influenced by various atmospheric, oceanic, and geographical factors, makes it a challenging phenomenon to forecast. This research employs data preprocessing techniques, outlier analysis, correlation analysis, feature selection, and several machine learning algorithms such as Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. The study focuses on developing the most accurate rainfall prediction model by utilizing machine learning and feature selection techniques. The Artificial Neural Network (ANN) achieves a maximum accuracy of 90% and 91% before and after feature selection, respectively. Furthermore, k-means clustering and Principal Component Analysis (PCA) are applied to examine regional rainfall patterns in Australia. Lastly, to make our proposed machine learning simpler and more usable for general people, we have formulated a web-based application system using Flask in our research paper. Overall, this research demonstrates the effectiveness of different machine-learning techniques in predicting rainfall using Australian weather data.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness

Hassan,

Rony,

Khan

et al. 2023

IEEE Access

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“…Our objective is to accurately predict new, unforeseen data. The most effective machine learning and deep learning methods for analysing the daily rainfall quantity forecasts have been chosen after evaluating many articles on rainfall prediction [8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…To summarise, the significant contributions of the discussed work are as follows: In this direction, a number of studies have been presented, such as those of Asif et al [1], and Luo et al [13]. Similarly, effective machine learning has been utilised to construct a rain forecast model in numerous research [1,6,14]. Osmani et al [15] suggest a novel approach for predicting monthly dry days at six target points using several machine learning (ML) techniques.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh

Rajab,

Farman,

Islam

et al. 2023

Water

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Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R2 score, root mean squared error and validation loss. According to this research’s findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R2 value of 0.76, while LSTM has a loss value of 0.09, respectively.

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“…Leveraging the capabilities of LSTM, (Khan et al, 2020) successfully predicted monthly temperature and rainfall in Bangladesh based on 115 years of weather data, spanning from 1901 to 2015. Building on the existing research, a machine learning model was specifically created for nationwide rainfall prediction (Ria et al, 2021). According to the Pearson Correlation Index, there is a significant negative correlation between the daily mean temperature and daily rainfall in Rangpur (Saha, 2020).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of Machine Learning based regression models for rainfall forecasting

Saleh,

Rasel

2024

Preprint

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Rainfall plays a crucial role in the water cycle, serving as a direct input for agricultural practices and water resource management. However, its patterns vary significantly across different regions, creating challenges for sustainable water use. This study focuses on the Rangpur district in northwestern Bangladesh, where irrigation heavily relies on unpredictable rainfall. To address this, this study employed three machine learning regression methods – Random Forest, Support Vector Machine, and Gradient Boosting Machine – using historical annual rainfall data from 1990 to 2020. The analysis was conducted on Google Colab, an open-source Python environment. Hyperparameter optimization via grid search was conducted on the three models to maximize prediction accuracy. The analysis revealed that the Random Forest model to be the most accurate for rainfall prediction in the Rangpur district. During the testing phase, it achieved an R-squared value of 0.75, indicating a strong correlation between predictions and actual rainfall. Interestingly, Gradient Boosting Machine outperformed Random Forest in the training phase, highlighting the importance of considering both training and testing performance for model selection. Additionally, Random Forest regression confirmed the strong relationship between predicted and observed rainfall by generating highest correlation (97%). This study demonstrates the effectiveness of Random Forest Regression for forecasting rainfall in Rangpur district. This knowledge can contribute to resilient water management strategies, enabling farmers and authorities to adapt irrigation practices and optimize resource allocation in response to predicted precipitation patterns. Future research could involve incorporating additional environmental variables into the model and exploring ensemble learning techniques for potentially further improving prediction accuracy.

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Standardization Of Rainfall Prediction In Bangladesh Using Machine Learning Approach

Cited by 7 publications

References 15 publications

Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness

Machine Learning-Based Rainfall Prediction: Unveiling Insights and Forecasting for Improved Preparedness

Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh

Performance evaluation of Machine Learning based regression models for rainfall forecasting

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