• Journal of Environmental Impact Assessment
• /
• v.26 no.5
• /
• pp.376-384
• /
• 2017

Bangladesh is a South Asian country with subtropical monsoonal climate between the intersection of the Indo-Himalayan and Indo-Chinese sub-regions, is known as biodiversity hotspot of the Asian region. The country has different types of forest like deciduous forest, evergreen forest, mixed forest, haor (wetlands) and mangrove forest. The natural beauty of the country is increased with the presence of so many rivers, longest sea beach of the world, green plants, critical hilly regions and green agricultural forest widely spread here and there. Sundarbans is the world largest mangrove forest and world natural heritage site declared by UNESCO in 1999 situated in Bangladesh and India. About 62 percent of this mangrove forest is situated in Bangladesh and there are so many plants and animals are found in this forest. To meet the increasing demand of the large population most of the natural ecosystem is now altered, deforestation rate is increased, natural habitat of the species is disturbed. Due to the imbalance of the climate and natural system many of the rare species of the world found this region is now endangered and some of the species are extinct. Directly or indirectly they are benefited from natural resources. At present time community, based ecotourism is also an important source of income for rural poor peoples. To protect the natural resources the government is now developed so many conservation acts and policy as well NGOs are also doing work for the conservation of ecosystem and biodiversity. At present transboundary pollutants and so many natural disasters also destruct the natural resources of Bangladesh.

• The Journal of Engineering Geology
• /
• v.4 no.2
• /
• pp.153-168
• /
• 1994

The status of groundwater development in Chungnam was studied with geological characteristics according to the measured data of Korean Rural Development Corporation. The data of 212 survey wells were used for the relation between catchment area and water discharge, and the data of 344 development wells for the relationships between well depth and discharge, between casing depth and discharge, between rock type and discharge, and the relation with lineaments density. The relationship between the catchment area and discharge does not show any special trend, and it is understood that groundwater of hard rock mass is not so much influenced by the surface catchment area. The relationship between well depth and discharge shows two different trends; discharge increasing with depth for alluvial groundwater, but no certain trend between depth and discharge for groundwater of hard rock zone. Discharge increases linearly with the casing depth, and it is reliable because the casing was installed in the weathered zone against well destruction. Generally the rock type does not show any difference of discharge, but the crystalline rocks such as granite and gneiss yield a little more discharge than the more porous rocks such as sedimentary rock or schist. It suggests that the effect of fracture zone is a major governing factor. In Hongsong and Puyo, there are similar in rock type and casing depth, but the big difference in average discharge. The big discharge of Hongsong is concordant with the higher intersection density and longer length of lineament in Hongsong than those of Puyo. Therefore the groundwater development strategy should be focused on the micro topography analysis and geophysical survey for the understanding of the fracture zone rather than catchment area or rock type.

• Journal of Korean Society of Transportation
• /
• v.32 no.3
• /
• pp.266-279
• /
• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.