Research Article
Analysis of Climatic Factors and Utilization of Machine Learning Techniques to Anticipate Humidity Levels in Northern Bangladesh
Most. Rubina Akter
,
Md. Habibur Rahman*
Issue:
Volume 10, Issue 1, June 2025
Pages:
1-20
Received:
30 January 2025
Accepted:
3 March 2025
Published:
5 March 2025
DOI:
10.11648/j.ajdmkd.20251001.11
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Views:
Abstract: Analyzing meteorological data in the northern region of Bangladesh is crucial for understanding various aspects influenced by humidity. This study employs machine learning algorithms, including k-nearest neighbor, Classification and Regression Trees, C5.0, Naive Bayes, Random Forest, and Support Vector Machine, to forecast the humidity of northern Bangladesh. Data from 1981 to 2020 from two meteorological stations, Rangpur and Dinajpur, were utilized. Results indicate that Rangpur had the highest average daily humidity (80.34%), while Dinajpur had the lowest (77.26%). Cloud amount correlates positively with humidity and inversely with temperature. The k-nearest neighbor, random forest, and support vector machine algorithms generally revealed better prediction performance than other algorithms. All things considered, the Random Forest model demonstrates superior performance on the testing dataset at both stations, achieving 70% accuracy, F1-score (75.85%), and a kappa value of approximately 53.3% at Rangpur Station, and 74% accuracy, F1-score (78.4%), and a kappa value of approximately 60% at Dinajpur Station. Subsequently, this study analyzes the best performance and accuracy of the random forest classification algorithms through k-fold cross-validation for predicting humidity. With this piece of information, it is anticipated that the study underscores the importance of random forest in predicting humidity and aiding decision-makers in water demand management, ecological balance, and health quality in the northern region of Bangladesh.
Abstract: Analyzing meteorological data in the northern region of Bangladesh is crucial for understanding various aspects influenced by humidity. This study employs machine learning algorithms, including k-nearest neighbor, Classification and Regression Trees, C5.0, Naive Bayes, Random Forest, and Support Vector Machine, to forecast the humidity of northern ...
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Research Article
Comparing Imports in FOB, CIF Terms of Delivery and Invoice Values and an Example on the Member States of the Economic Cooperation Organization
Aylin Kolbaşı*
Issue:
Volume 10, Issue 1, June 2025
Pages:
21-36
Received:
4 March 2025
Accepted:
17 March 2025
Published:
29 April 2025
DOI:
10.11648/j.ajdmkd.20251001.12
Downloads:
Views:
Abstract: Foreign trade data and indicators are important resources for many economic analysis. In particular, the Central Bank of the Republic of Türkiye uses these data for the calculation of balance of payments. Export data published by Turkish Statistical Office (TURKSTAT) are calculated according to free on board (FOB terms of delivery) and the import data are calculated according to the cost of goods, insurance and freight (CIF terms of delivery). In the balance of payments account calculated by the Central Bank, export and import is used by FOB terms of delivery. Therefore, imports data should be calculated according to FOB terms of delivery at the same time. However, international methodological studies have concluded that valuation using invoice values is more compatible with the concepts and definitions of the system of national accounts and the balance of payments, and therefore the use of invoice values is recommended. In line with international methodological recommendations, this study compares the import balance values calculated in terms of FOB and CIF terms of delivery with the values calculated in terms of invoice value and reveals the difference between them. For this comparison, import values to the member countries of the Organisation for Economic Co-operation and Development are taken into account.
Abstract: Foreign trade data and indicators are important resources for many economic analysis. In particular, the Central Bank of the Republic of Türkiye uses these data for the calculation of balance of payments. Export data published by Turkish Statistical Office (TURKSTAT) are calculated according to free on board (FOB terms of delivery) and the import d...
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