ABSTRACT

The planning and management of water resources are becoming more and more important, and the forecast of water demand as the prerequisite and foundation of the entire planning has become a very important task in agricultural development. This paper combines the particle swarm algorithm to construct the agricultural water resource demand forecasting model, analyzes the shortcomings of the traditional particle swarm algorithm, and makes appropriate improvements to the quantum particle swarm algorithm. Moreover, this paper constructs the functional structure of the agricultural water resource demand forecast model based on the forecast demand of water resources, and analyzes the application process of the particle swarm algorithm in the system of this paper. After the model is constructed, the performance of the model is verified, and the simulation test is designed to evaluate the effect of system forecast with actual data. At the same time, this paper uses the model constructed in this paper to analyze the factors affecting water resources forecast demand. From the results of the experimental analysis, it can be seen that the model constructed in this paper is more effective in the forecast of water resources demand.

摘要

水资源的规划和管理越来越重要，需水量预测作为整个规划的前提和基础，已成为农业发展中一项十分重要的工作。本文结合粒子群算法构建了农业水资源需求预测模型，分析了传统粒子群算法的不足，并对量子粒子群算法进行了适当的改进。此外,本文构建了基于水资源需求预测的农业水资源需求预测模型的功能结构,并分析了粒子群算法在本文系统中的应用过程。模型构建完成后，验证模型的性能，并设计了仿真测试，以实际数据评估系统预测的效果。同时，利用本文构建的模型，分析影响水资源预测需求的因素。从实验分析结果可以看出，本文构建的模型在水资源需求预测方面更为有效。