Abstract

This research is motivated by flood conditions that often occur in low-lying areas in big cities such as Jakarta and Semarang. Notification and early detection of floods are often delayed and carried out manually, and are not integrated with hydroclimatological data, and disaster mitigation, so that they are often delayed and cannot be anticipated by upstream areas. Therefore, it is very important to be able to develop early warning tools so that floods can be detected early and can be anticipated in upstream areas. This research method uses quantitative data analysis of flood prediction studies and the design of the FEDS prototype that uses several sensors for IoT-based flood early detection. The study was conducted along the Ciliwung River from Katulampa, Bogor to the MT Haryono area, Jakarta, Indonesia, using secondary and primary data. Secondary data in the form of water level, river discharge, flood discharge plan, length of the river, rainfall and the area of the watershed. This study uses quantitative data analysis by performing simple and multivariate regression calculations, hydrograph analysis and the curve is the intensity duration frequency (IDF) curve. The results show that flood discharge in Jakarta will increase due to various reasons, one of which is a higher intensity of rainfall in the future and a lower area that can absorb excess water. By comparing the measurement of water level using HEC RAS with data on the floodgates. MT. Haryono, it can be seen that the results are not too different. The Flood Early Detection System (FEDS) is a tool to provide accurate and real time flood early information, so that people living in areas around rivers can prepare early if there is a possibility of flooding. This tool uses an environmentally friendly 20 WP solar power supply, can detect water level, rainfall, humidity and ambient air temperature. This tool uses Ultrasonic Sensor, Flow Sensor, rain sensor and the IoTbased Blynk application, which is expected to be able to provide early information on flood hazard predictions in downstream locations in a practical, accurate and real time manner.