【摘要】：隨著經(jīng)濟(jì)的發(fā)展和居民生活水平的提高,人口增長、環(huán)境污染、以及城鎮(zhèn)化等因素使得水資源供需之間的矛盾愈加突出,用水安全問題日益凸顯。研究農(nóng)村居民用水行為,可以提高當(dāng)前農(nóng)村居民的節(jié)水意識并改善水資源管理薄弱現(xiàn)狀。本文提出的農(nóng)村居民用水行為識別方法可以準(zhǔn)確的識別居民用水行為,改善當(dāng)前用水基礎(chǔ)設(shè)施。本文通過分析幾種典型的居民用水事件的流量特點(diǎn)。研究了農(nóng)村居民用水行為的識別方法。具體的工作如下:從訓(xùn)練集合中提取不同用水行為的流量特征,采用由左到右隱馬爾可夫模型(Hidden Markov Model, HMM)建立不同類型居民用水行為的識別模型,將測試數(shù)據(jù)輸入訓(xùn)練好的HMM中對居民用水行為進(jìn)行識別,根據(jù)居民此刻用水的流量序列識別居民此時(shí)的用水事件。為提高應(yīng)用HMM的用水行為識別結(jié)果的準(zhǔn)確度,本文將HMM和時(shí)間概率函數(shù)結(jié)合起來,得出該方法的識別結(jié)果。選定人工神經(jīng)網(wǎng)絡(luò)(Artificial NeuralNetworks,ANN)算法,設(shè)計(jì)BP神經(jīng)網(wǎng)絡(luò)(Back Propagation, BP)網(wǎng)絡(luò)結(jié)構(gòu),確定BP網(wǎng)絡(luò)訓(xùn)練參數(shù),使用BP神經(jīng)網(wǎng)絡(luò)建立居民用水行為的識別模型,最后將測試數(shù)據(jù)輸入訓(xùn)練好的BP神經(jīng)網(wǎng)絡(luò)模型中對居民用水行為進(jìn)行識別,得出識別的結(jié)果。研究結(jié)果表明:對不同流量模式的用水事件采用HMM和時(shí)間概率函數(shù)的組合模型能得出更準(zhǔn)確的識別結(jié)果;相似流量模式的用水事件采用BP神經(jīng)網(wǎng)絡(luò)模型識別能得到較高的識別準(zhǔn)確度。
[Abstract]:With the development of economy and the improvement of residents' living standard, population growth, environmental pollution and urbanization make the contradiction between supply and demand of water resources more and more prominent, and the problem of water security is becoming more and more prominent. The study of water use behavior of rural residents can improve the awareness of water saving and improve the weak situation of water resources management. The method proposed in this paper can accurately identify the water use behavior of rural residents and improve the current water use infrastructure. In this paper, the flow characteristics of several typical water use events are analyzed. The identification method of rural residents' water use behavior was studied. The specific work is as follows: the flow characteristics of different water use behaviors are extracted from the training set, and the identification models of different types of residents' water use behavior are established by using left to right hidden Markov model (Hidden Markov Model, HMM). The test data are input into the trained HMM to identify the residents' water use behavior, and the residents' water use events are identified according to the flow sequence of the residents' water consumption at the moment. In order to improve the accuracy of the recognition results of water use behavior using HMM, this paper combines HMM with time probability function, and obtains the recognition results of this method. The artificial neural network (Artificial NeuralNetworks,ANN) algorithm is selected, the (Back Propagation, BP) network structure of BP neural network is designed, the training parameters of BP network are determined, and the identification model of residents' water consumption behavior is established by using BP neural network. Finally, the test data are input into the trained BP neural network model to identify the behavior of residents' water use, and the recognition results are obtained. The results show that the combination model of HMM and time probability function can obtain more accurate identification results for different water flow patterns. The BP neural network model can be used to identify water events with similar flow patterns.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183

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本文編號：2430236