Abstract
Water is a fundamental natural resource while agriculture water guarantees the grain output, which shows that the utilization and management of water resource have a significant practical meaning. Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience. The current research on water resource resilience remains to focus on qualitative analysis and the quantitative analysis is still in the primary stage, thus, according to the above issues, projection pursuit classification model is brought forward. With the help of artificial fish-swarm algorithm (AFSA), it optimizes the projection index function, seeks for the optimal projection direction, and improves AFSA with the application of self-adaptive artificial fish step and crowding factor. Taking Hongxinglong Administration of Heilongjiang as the research base and on the basis of improving AFSA, it established the evaluation of projection pursuit classification model to agriculture water resource system resilience besides the proceeding analysis of projection pursuit classification model on accelerating genetic algorithm. The research shows that the water resource resilience of Hongxinglong is the best than Raohe Farm, and the last 597 Farm. And the further analysis shows that the key driving factors influencing agricultural water resource resilience are precipitation and agriculture water consumption. The research result reveals the restoring situation of the local water resource system, providing foundation for agriculture water resource management.
Highlights
With the rapid growth of population and fast development of economy, the agricultural water supply and demand contradiction are increasing, resulting in a series of ecological and environmental problems, such as water pollution, falling of groundwater level, and obvious deterioration of regional agricultural water resource system
Regional agricultural water resource system features with unpredictable, self-organization, and non-linear which lays a certain difficulty on the evaluation of regional agriculture water resource resilience
Taking Hongxinglong Administration as the research base, it applies for projection pursuit classification model on adaptive artificial fish-swarm algorithm (AAFSA–Projection pursuit classification model (PPC)) to evaluate the resilience of regional agricultural water resource system, and to provide a scientific and reasonable research mode for water resource system resilience research
Summary
With the rapid growth of population and fast development of economy, the agricultural water supply and demand contradiction are increasing, resulting in a series of ecological and environmental problems, such as water pollution, falling of groundwater level, and obvious deterioration of regional agricultural water resource system. The research on water resource system resilience in and abroad remains in the model of conceptual level and case analysis; the study on resilience diagnosis method is still weak, and the resilience formation mechanism and influencing factor study are still in shortage, and the practical guiding from resilience diagnosis result still needs to be strengthened. Taking Hongxinglong Administration as the research base, it applies for projection pursuit classification model on adaptive artificial fish-swarm algorithm (AAFSA–PPC) to evaluate the resilience of regional agricultural water resource system, and to provide a scientific and reasonable research mode for water resource system resilience research. The target of this paper is to apply AFSA and accelerating genetic algorithm for evaluation on agricultural water resource system resilience of Hongxinglong Administration in Heilongjiang Province, reveal the local water resource resilience condition, and analyze the main driving factor influencing water resource resilience and bring forward improvement suggestion. The first part of the paper is brief introduction, the second part is research method and models including AAFSA, RAGA and PPC, the third part is the case study including research area, data collection and evaluation grading, and the calculation result and discussion, and the final part is the summary and the further study direction
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