Bangladeshi Taka Research Articles

Models for Cyclone Shelter Planning in Bangladesh

ABSTRACT The coastal areas of Bangladesh are prone to disaster due to storm surge floods generated by tropical cyclones which form in the Bay of Bengal. A master plan, prepared at the request of the Government of Bangladesh, estimated that 2,500 new cyclone shelters are needed to provide adequate protection for the projected population in the year 2002. Since this will require an investment of 300 million USD (12 billion Bangladeshi Taka) which may not be possible in the near future, a means of identifying priority areas is needed. To this end, a mathematical programming model is developed. The model relies on input from hydrologic simulation models which determine the spatial distribution of flood depth, as well as from surveys and an infrastructure inventory which provide an estimate of population vulnerability. Using the mathematical programming model, a multi-objective analysis is conducted to explore some trade-offs involved in allocating shelters among a number of planning units. Results indicate that a significant trade-off exists between minimizing overall risk and maximizing regional equity when the available budget is small.

DEVELOPING A PREDICTIVE MODEL TO PREDICT THE EXCHANGE RATE OF BANGLADESHI TAKA (BDT) AGAINST US DOLLAR (USD)

This study is concerned with applying machine learning to develop a predictive model to forecast the exchange rate of Bangladeshi Taka (BDT) against US dollar (USD). There are studies that showed that machine learning and deep learning methods fit well in predicting exchange rate and outperform traditional models. However, in Bangladesh, machine-learning based predictive modeling for the said purpose has remained an uncharted area so far and this paper attempts to fulfill this research gap. For this, the economic data of Bangladesh for 11 fiscal years (from 2009-10 to 2019-20) were collected from the central bank database and analyzed using the analytics tool-KNIME. The findings suggest that (1) decision tree makes a comparatively better predictive model with very high level of accuracy, (2) feature selection technique does not contribute to the improvement of accuracy, and (3) to get the optimal model, multiple experiments with different combinations of variables should be conducted. The findings of this paper can be of immense help to several stakeholders including the country’s central bank, policymakers, academicians, and researchers as it can be the base point to explore novel avenues of research and to advance the application of predictive modeling in new areas.