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节水潜力预测研究综述
引用本文:刘凡,李逸云,李泽文,毛莺池.节水潜力预测研究综述[J].水利经济,2018,36(6):41-47.
作者姓名:刘凡  李逸云  李泽文  毛莺池
作者单位:河海大学计算机与信息学院, 江苏 南京 211100; 南通河海大学海洋与近海工程研究院, 江苏 南通 226300,河海大学计算机与信息学院, 江苏 南京 211100,河海大学计算机与信息学院, 江苏 南京 211100,河海大学计算机与信息学院, 江苏 南京 211100
基金项目:国家自然科学基金(61602150);中国博士后科学基金资助项目(2017T100323);南通市科技计划项目(GY12017014)
摘    要:系统整理并分析了不同领域节水潜力预测方法的分类、原理、适用范围及研究现状,帮助从业人员针对实际问题快速选择模型。分析认为,基于公式模型的节水潜力预测方法收集数据较少,操作简捷,使用范围较广,但精确性不高;基于机器学习的节水潜力预测方法虽然收集的数据种类和数量较多,但构造出的预测模型使用范围广,精度高。针对节水潜力预测的现存问题,总结分析了其发展趋势。未来节水潜力预测的研究应根据不同产业和地域特点,引入深度学习、大数据等新技术,实现精细化节水潜力预测;同时加快完善基于互联网的节水潜力预测应用,实现集成数据收集、处理、预测、发布等功能于一体的节水社会化服务。

关 键 词:节水潜力预测  机器学习  深度学习  大数据  互联网
收稿时间:2018/7/26 0:00:00

Review of prediction of water-saving potentials
LIU Fan,LI Yiyun,LI Zewen and MAO Yingchi.Review of prediction of water-saving potentials[J].Journal of Economics of Water Resources,2018,36(6):41-47.
Authors:LIU Fan  LI Yiyun  LI Zewen and MAO Yingchi
Institution:College of Computer and Information, Hohai University, Nanjing 211100, China;Nantong Ocean and Coastal Engineering Research Institute, Hohai University, Nantong 226300, China,College of Computer and Information, Hohai University, Nanjing 211100, China,College of Computer and Information, Hohai University, Nanjing 211100, China and College of Computer and Information, Hohai University, Nanjing 211100, China
Abstract:The classification, principle, scope of application and research status of prediction methods for water-saving potentials in different fields are summarized so as to help researchers to select models more quickly for practical problems. The results show that the formula model-based methods have been widely used and are easy to operate with less data. However, their precisions are not high. The machine learning-based methods can be widely utilized with high precision although they require more data. In response to the existing problems in the prediction of water-saving potentials, the development trend is summarized and analyzed. In the future, some new technologies such as deep learning, big data will be introduced into the researches on the prediction of water-saving potentials according to different industries and regional characteristics. They will look forward to achieve refined water saving potential prediction. In addition, accelerating the application of Internet-based prediction of water-saving potential will realize the water-saving socialization services which integrate the functions such as data collection, processing, forecasting and publishing.
Keywords:prediction of water-saving potential  machine learning  deep learning  big data  internet  review
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