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.
Flood level indicator and risk warning system for remote location monitoring using flood observatory system
Flooding is a great treat towards mankind as it is also considered one of the most devastating natural disasters in the world. Flooding is not any abnormal scenario worldwide, since flooding results in great damages to agriculture land, residential area and even cities with high cost in lives and towards the economy of the country. The government has to spend tons of money in flood mitigation plans in afford to help the victims and also to reduce the number in the long run. Most of the flood mitigating plans has high cost and only can be implemented base on priority. Baring the cost and safety measures, this paper highlights the Flood Observatory System (FOS) as a warning and alert system to efficiently monitor the critical flood prone areas in real time basis. FOS can be deployed in flood prone areas in afford to create a well-used standard for remote flood observation systems. The ability to receive real time information on flood level empowers both government and private organizations to react to imminent danger in an effective manner. With the real time flood information, allows public safety organizations and other emergency managers to effectively plan their resource deployment within the limited time of alert. Warning as flood rises could be used to save life's and properties in many ways can help such organization and government to spend sufficient amount of money in restoration process. The simple and practicality of a system should be useful in all means towards mankind.
Simplex Particle Swarm Optimization Algorithm and Its Application
An improved particle swarm optimization algorithm---simplex particle swarm optimization algorithm(SPSO)was proposed based on PSO and simplex method(SM).Then,SPSO,PSO and SM are used to resolve five widely used test functions' optimization problems.Results show that SPSO has greater efficiency and better performance than PSO and SM.Next,SPSO is applied to train artificial neural network to construct a practical soft-sensor of gasoline endpoint of main fractionator of fluid catalytic cracking unit(FCCU).The obtained results and comparison with actual industrial data indicate that the new method proposed by this paper is feasible and effective in soft-sensor of gasoline endpoint.