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Identification of Disaster Prone Areas-A Machine Learning Approach

机译:易受灾地区的识别-机器学习方法

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In the earlier days when natural disasters are occurred that information is communicated through phone calls, telegram, direct observations or personal interview due to which relief operations used to get late; thus human lives and animal mortality will get increases and sufferings eventually increased. The internet technologies developed are used now days to some extent to control the rate of the sufferings. Tweets are the fast and real-time sources for information. We perform various approaches to identify which process can work faster compare to others. First we collect data from social media which is considered as fastest medium to reach vast number of people. Then we classify according to our needs and make clusters using algorithm. For better understanding, we use various tools to visualize and to identify the specified region to provide necessities like food, shelter and medicines in an earliest possible time.
机译:在自然灾害发生的较早时期,信息是通过电话,电报,直接观察或个人访谈来传达的,由于这些信息,救援行动过去常常迟到;因此,人类的生命和动物的死亡率将会增加,而痛苦最终会增加。如今,发展起来的互联网技术已在一定程度上用于控制痛苦的程度。推文是快速,实时的信息来源。我们执行各种方法来确定哪个流程可以比其他流程更快地工作。首先,我们从社交媒体收集数据,而社交媒体被认为是覆盖大量人群的最快媒介。然后我们根据需要进行分类,并使用算法进行聚类。为了更好地理解,我们使用各种工具来可视化和识别指定区域,以便在尽可能短的时间内提供必需品,例如食物,庇护所和药品。

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