Flood Early Warning System Using Cognitive Artificial Intelligence: The Design of AWLR Sensor

Abstract

Flood is one of the disasters that have a big impact in Indonesia. Flooding can affect social, economic, agricultural aspects, etc. Flood disaster is a difficult thing to avoid, the best approach is to reduce the impact caused. Research on flood mitigation began to move to an artificial intelligence approach to providing information on early warning systems (EWS). EWS is a series of systems that function to inform of the occurrence of natural events in the form of floods that will notify the disaster-related floods that will occur. This research will build an intelligent system for building the EWS system. A smart AWS certainly requires various sensors to collect data for later analysis. One of the most important sensors to the construction of the EWS system is the automatic water level recorder (AWLR) sensor. In this paper will specifically discuss the design stages of the AWLR in conducting water level monitoring at the water-gate dam. The function of AWLR sensor is for monitoring and recording in a database with real-time sensing. The results of this research show that the low-cost AWLR sensor has reduced processing time by 92.7% compared to conventional data processing.