An Inference Approach of Flood Level Detection and Alert System: Flood-Induced Simulated Environment

Abstract

Many kinds of research focused on the flood detection and monitoring, flood management, flood risk management and flood forecasting in urban areas, wherein a large number of populations lies chaos in mobility is high. Owing to natural disasters, flooding in these regions can lead to an increase in mortality rates. This project is primarily focused on the detection of a flood by installing a flood detector device with a camera beside the bridge column. The camera is facing the three lines with different colors. If one of the colors was tempered by the river water, the device will send an alarm to the community that the water level in the river is high. This aims to alert the community and the authorities to be aware and be ready for the approaching flood. Flood-Level Detection and Alert System proved 87.1%, 73.6%, and 95.69% testing accuracy of Green, Blue, and Red respectively. Overall, the accuracy of the whole system produced 85.46%.

CoCo-Dropping: A Color Composition Extraction of Gamefowl’s Dropping Using Image Processing

“…The color composition of the recognized droppings will be extracted for this study. Study of [16]- [20] used also the power of image processing as a tool for data analysis.…”

Testing the Concept of Mitigating Urban Flooding with Permeable Road: Case Study of Tong Wei Tah Street, Kuching City, Sarawak, Malaysia

“…This paper intends to model the flooding processes that involve water transitioning between the waterway to permeable road. Evaluation of the flood model depends on the flood level [6], which is generated by rainfall delivered to the affected area. In this regard, the rainfall data could be in terms of statistically generated design rainfall which the authors had previously reported [7]; or alternatively using a historical rainfall event, such as the one described in the following section.…”