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Identifying the Flooded Area Using Deep Learning Model
In recent years many countries were still affected by the flood and related disastrous. People who live in low-level areas and people who are living near the water resource area such as lakes, dams, river, and other water reservoirs these areas are worst affected by the flood every year. It is due to improper planning in constructing a building as well as other public constructions (i.e proper sewage system or drainage system). There are many reasons how floods occur but these two reasons are the most important reason because floods are occurred due to heavy rain. However, in today’s situation even moderate rainfall cause flood due to there is no space for the rainwater to drain or reach the coastal area. This work aims to identify these areas which may face damages due to flood. Furthermore, it provides information as well as decides whether the particular area is safe from damages due to flood.
Identifying the Flooded Area Using Deep Learning Model
In recent years many countries were still affected by the flood and related disastrous. People who live in low-level areas and people who are living near the water resource area such as lakes, dams, river, and other water reservoirs these areas are worst affected by the flood every year. It is due to improper planning in constructing a building as well as other public constructions (i.e proper sewage system or drainage system). There are many reasons how floods occur but these two reasons are the most important reason because floods are occurred due to heavy rain. However, in today’s situation even moderate rainfall cause flood due to there is no space for the rainwater to drain or reach the coastal area. This work aims to identify these areas which may face damages due to flood. Furthermore, it provides information as well as decides whether the particular area is safe from damages due to flood.
Identifying the Flooded Area Using Deep Learning Model
Raj, Jeberson Retna (Autor:in) / Charless, Immanuael (Autor:in) / Latheef, Mohamed Ayman (Autor:in) / Srinivasulu, Senduru (Autor:in)
28.04.2021
1932407 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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