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Intelligent modeling of urban water supply prediction

机译：城市供水预测智能建模

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摘要

To reduce energy and water, water supply company need estimate future water consumption according to the record of daily water supply, and best arrange future production planning and control, water consumption is uncertainty and is strong non-linear time series, water consumption prediction estimation is more concerned by academics, it is predicted through various methods, multiple regression analysis and gray forecast are the most common method at present, these methods are difficult to give a satisfactory result according to the characteristic of nonlinear and time varying. In accordance with the disadvantage above methods, a new intelligent model is presented to predict accurately water consumption of a city based on optimal common machine learning algorithm- support vector machine in this paper. Complex and strong nonlinear water consumption was simulated by network design and conformation of support vector machine learning algorithm and the optimized support vector machine parameters were selected by the method of network searching and cross validation according to existing data. Compared the errors with output value of the optimized model and output value from grey model, support vector machine whose parameter was optimized with cross validation had excellent ability of nonlinear modeling and generalization. It provides a simple and feasible intelligent approach for water consumption prediction.
机译：为了减少能源和水，供水公司需要根据日水供应记录估计未来用水量，并最佳安排未来的生产规划和控制，耗水量是不确定性，是强大的非线性时间序列，耗水预测估计是通过学术界更加关注，通过各种方法预测，多元回归分析和灰色预测是目前最常见的方法，这些方法难以根据非线性和时间变化的特性给出令人满意的结果。根据上述方法的缺点，提出了一种基于本文最优公共机器学习算法 - 支持向量机的基于最优公共机器学习算法的城市精确耗水的新智能模型。通过网络设计和支持传染媒介机器学习算法的网络设计和构象来模拟复合和强的非线性耗水量，并根据现有数据的网络搜索和交叉验证的方法选择优化的支持向量机参数。与灰色型号的优化模型和输出值的输出值与输出值进行比较，支持向量机，其参数与交叉验证进行优化，具有出色的非线性建模和泛化能力。它提供了一种简单可行的智能方法，用于耗水预测。

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