Rain Disaster Management through Novel Approach of Data Science Utilizing Python as a Programming Language: Utilizing Python for Efficient Disaster Risk Management in Urban Flooding (Journal of Advances and Scholarly Researches in Allied Education, 2019)
The impact of flooding is the world's most significant disasters. Over half of destruction from global floods occurs in Asia. Flood causes are because of natural factors like rainfall, flooding tides, human factors like channel blockage or excessive use, headwater deforestation, etc. Floods lead to loss of life property damage. Increasing population results in more urbanization, more impermeable, less erosion, higher peak runoff floods. Issues are becoming more urgent because of more serious regular flooding possibly caused by climate change, socio-economic damage, impacted population, public outrage limited funds. Prevention mitigation of flood damage include structural flood control measures such as dam or river dikes construction non-structural measures such as flood prediction warning, flood hazard risk management, community engagement organizational structure, etc. This paper describes principles, strategies, plans procedures for integrated disaster risk management in urban floods. In most developing countries, government flood disaster management (DM) activities are very limited involvement of non-governmental agencies private sectors. Reactive to enhance management efficiency reduce life property losses. Proactive DM requires more involvement from different governments, non-governmental private agencies, public involvement. Its time, more budget, more equipment, facilities human resources, resulting in integration of flood DM for both long-term short-term activities. Tools for efficient data manipulation analysis have become significant for (training) statistics with Data Science as a recent trend. Programming language of Python begins most popular data analysis tools, replacing in many cases. We report on a short case study utilizing Python on basic statistical assessment of real-world air quality data.