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Flooding causes significant damage to lives and property. Moreover, it affects the economy and the lifestyles of people in society for both the short term and long term. As a consequence, this research aimed to demonstrate techniques and flood detection analysis through digital images and web application development for receiving reports and inspection data about flood situations in every area. The process requires crowdsource data and uses 3S technology so it can receive accurate, real-time data for making decisions in flood management. It also offers aid to the people in the disaster areas. In this research, a convolutional neural network was applied for flood detection and classification using digital images and data from people or the victims. According to the study, it was found that the convolutional neural network for flood classification has data accuracy at high levels of 95. 50%, 93. 00%, 97. 89%, and 0. 91, which are the results of accuracy, producer accuracy, user accuracy, and kappa statistics, respectively. Besides, the use of this technique saves costs, time, and labour. Furthermore, the method could be applied to other disasters such as landslides, earthquakes, and fires. It is able to monitor the incidents in each type of disaster and also examine the damaged site after the incident. [ABSTRACT FROM AUTHOR]