Forecasting the Average Temperature Rise in Bangladesh: A Time Series Analysis

Paul, Sneha

doi:10.3329/jes.v11i1.49549

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Dong, et al [21] enhanced short-term forecasting of daily precipitation using the XGBoost model combined with multi-factor bias correction for numerical weather prediction (NWP). Paul and Roy [22] developed a machine learning-based time series forecasting model to predict temperature any year in the future of Bangladesh.…”

Section: Related Workmentioning

confidence: 99%

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Hussain,

Tripura,

Aslam

2024

Preprint

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Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

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Section: Related Workmentioning

confidence: 99%

Rainfall and Daily Average Temperature Prediction using Machine Learning: A Case Study of Bangladesh

Hussain,

Tripura,

Aslam

2024

Preprint

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“…Their study indicated that climate change would cause continuous increases of rainfall, temperature and weather-related extreme events. Karmakar (2019) found patterns of climate change and its impacts in Future Projection of Mean Temperature ….. northwestern Bangladesh and in the very recent, Paul et al (2020) studied the past 100 years temperature data of Bangladesh and it found that in this period temperature of Bangladesh was risen by 1 0 C on average and an increment of close to another 1 0 C would be in the average temperature of Bangladesh from the prediction of the model in next century. Alamgir et al (2019) analyzed that the greatest increase in maximum and minimum temperature was found in the more northern regions and the lowest increase was found in the southeast coastal regions.…”

Section: Introductionmentioning

confidence: 99%

Future Projection of Mean Temperature of Post-Monsoon Season Over Bangladesh Using Statistical Downscaling of Global Climate Models

Rashid¹,

Hossain

2020

J. of Eng. Sci.

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In statistical downscaling technique, regional or local information are derived by determining a statistical model which relates large-scale climate variables or predictors generated by Global Climate Models (GCMs) to regional and local variables or predictands. In this paper, the results of GCMs were statistically downscaled to produce future climate projections of mean temperature in the post-monsoon season (October and November), for the time periods 2021-2050 and 2071-2100 for Bangladesh. The future climate projections are based on the three emission scenarios RCP2.6, RCP4.5 and RCP8.5 provided by the fifth Coupled Model Intercomparison Project (CMIP5). This paper established a method to analyze GCMs for use in statistical downscaling and utilized fifteen GCMs. The GCMs were assessed based upon their performance in simulated past climate in Bangladesh and adjoining areas. Downscaling was undertaken by linking large scale climate variables, taken from the ERA-Interim (resolution 79 km) reanalysis temperature, a gridded data set incorporating observations and climate models, to local scale observations. Overall, all fifteen GCMs, via statistical downscaling, show that mean temperature of the post-monsoon season in Bangladesh will increase under future climate scenarios. Comparing the ensemble of future projections with the reference period (1981- 2010), the mean post-monsoon temperature in Bangladesh is projected for RCP8.5 showing warming by 0.310C in near future and 1.790C in far future. On the other hand, estimated warming is 0.390C in near future and 1.140C is far future for RCP4.5. Low emission scenarios RCP2.6, near future temperature is nearly same the far future temperature. Journal of Engineering Science 11(2), 2020, 27-35

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