Study on scheme optimization of urban flood disaster prevention and reduction
The decision in flood disaster prevention and reduction will affect the evaluation of flood disaster directly. How to apply the natural, economic and social information, through anticipatory analysis, to ensure the minimum damage after practicing certain optimum scheme is a core problem in the flood disaster prevention and reduction decision-making. In this paper, the factors affecting this decision-making are analyzed, the fuzzy optimum selection and neural network are applied in scheme optimization of urban flood disaster prevention and reduction, and the model of scheme optimization is established.
Designing Early Warning Flood Detection and Monitoring System via IoT
Flooding is one of the biggest natural disasters that occurs frequently around the world. It can occur without warning and the after effect of it leaves great damage to the surrounding environment and exposes life threatening to citizen. Therefore, early flood detection and monitoring system with the implementation of Internet of Things and Global Positioning System is proposed in order to reduce the risks that may cause flooding. The aim of this project is to provide the information of a current water level in a drain. When water level increases to a certain level, the system will send a warning notification to users indicating three categories of water level, which are safe, warning and critical level. This system contains an ultrasonic sensor to detect the current water level and at the same time allows users to observe the period of the water level from their phone so that users are more aware of when flooding ought to happen. Moreover, the system consists of a flooding avoidance method that requires the usage of a solenoid as a shutter valve of the drain and water pump to pump out excessive water flow to a suitable place for water release purposes.
Application of Kernel-Based Methods to Soft Sensor Modeling of Selectivity to Acrylonitrile
Principles of kernel-based methods and two kernel-based statistical modeling techniques are introduced. Soft sensor modeling of selectivity to acrylonitrile using kernel PLS and kernel PCR is proposed to cope with the nonlinearity and multi high dimension of input and collinearity problem of process. It is found that the performance of kernel-based methods is superior to linear statistical model and that of kernel PLS is also superior to kernel PCR.