Performance of Flood Early Detection System (FEDS) and Artificial Neural Network on Predicting Flood in the City
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Abstract
An integrated flood detection system that is easily accessible to the public is one of the efforts to reduce the impact of flooding in flood-prone locations. The performance of flood detectors integrated with the internet helps make it easy Public for access information about possibility happening flood. Information about bulk rain, high water level, the water discharge will Becomes indication possibility happening downstream flooding. Tool prototype detector flood this be equipped with temperature and humidity sensor as addition information for society. Research results show that performance tool detector flood already good because capable give information related to data that can be made indication possibility happening flood. Sensors used have score small mistake and after calibrated got score constant for standardize results testing. Bulk sensor test results rain produces an average error of 3% and after calibration obtained constant of 1.03. High sensor test results water level has an average error of 1.07% and after calibrated obtained score constant of 0.98. Humidity sensor testing have the average error value is 3% and after calibration obtained score constant 1.03
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