Theory And Technology Of Risk Management For Agricultural Drought Disaster

With rapid economy development and population increase, it is more and more important for people to take efficient operations for agricultural drought disaster. We have known that drought disaster can not be avoided and only reduce loss by all means. Traditional methods get inapplicable under serious condition of water deficit. It is necessary to take risk management for drought disaster. How to realize efficient management for drought? This is the hotspot and difficulty in water resources field. Agriculture is basis of national economy in our country. It is very urgent to make research to risk management of agriculture drought thoroughly. This paper establishes the system of risk management for agricultural drought disaster on basis of the research trend in the world, puts forward methods for describe regulations of agricultural drought in time and space based on uncertain and incomplete theory, and suggests risk assessment model for agricultural drought with the technique of data mining. The main results of this paper are as follows:(1) Point out that drought definition relates to two aspects of water demand and water supply, and abnormal feature of drought should be considered. Based on this idea, improved drought definition that is suit to practice is supposed. Suggest extended risk and extended risk of natural disaster that describe conceptions by disaster influence and forecasting liability. On the basis of extended risk, system of risk management for agricultural drought is established.(2) Cloud theory and information diffusion technology are used to take research to danger of agricultural drought. Models with these theory and technology can take advantage of more information, describe greatly non-linear relations between anniversary precipitations, and get better results than other classic methods.(3) Point out it is the most important that drought evaluation for agriculture should correctly reflect the loss caused by the agricultural drought, and establishes the quantitative risk assessment models for agricultural drought that are static model and dynamic model respectively. These models can not only describe the drought extent quantitatively but also reflect the agricultural lose caused by drought. Model of genetic programming is established to describe water production function. Model that is more flexible and intelligent can find the optimal structure automatically by evolutionary algorithm and can avoid the inconvenience of establishing concrete formula.(4) Put forward risk decision model for agricultural drought based on group decision. This model can reduce faults result from the prejudice of individual. Improve traditional particle swarm optimization and apply it into optimal design of agricultural water resources. Suggest carrying capability of agricultural water resources and establish corresponding model.(5) Make research to probability risk of agricultural drought based neural networks optimized by chaos methods, which can avoid the inconvenience of establishing concrete mathematical formulas and the calculation of parameters. Take advantage of simulation technology to realize the benefits calculation of irrigation project. Apply fuzzy matter-element into decision of irrigation schemes, and realize synthetic assessment for irrigation schemes.