本文選題：水資源系統(tǒng) + 預(yù)測(cè)　； 參考：《合肥工業(yè)大學(xué)》2017年碩士論文

【摘要】：水是人類賴以生存和發(fā)展的不可替代的重要資源。進(jìn)入21世紀(jì)以來(lái),許多國(guó)家都面臨著水資源危機(jī)的挑戰(zhàn),水資源短缺、水體污染、洪澇災(zāi)害等嚴(yán)重阻礙了我國(guó)經(jīng)濟(jì)和社會(huì)的發(fā)展,造成一系列社會(huì)問(wèn)題。研究水資源預(yù)測(cè)問(wèn)題,對(duì)實(shí)現(xiàn)水資源的可持續(xù)利用、促進(jìn)水資源與經(jīng)濟(jì)、社會(huì)和生態(tài)環(huán)境的可持續(xù)發(fā)展、緩解我國(guó)水資源危機(jī)均具有十分重要的意義。在水資源預(yù)測(cè)過(guò)程中的主要問(wèn)題是如何有效處理不確定性,而集對(duì)分析方法在處理這些問(wèn)題時(shí)表現(xiàn)出明顯的優(yōu)勢(shì)�；诖�,論文主要在集對(duì)分析理論的基礎(chǔ)上,研究了水資源系統(tǒng)預(yù)測(cè)方法及其應(yīng)用問(wèn)題,取得如下研究成果:(1)針對(duì)目前研究中徑流級(jí)別劃分存在不確定性,主張?jiān)趶搅鞣旨?jí)時(shí)應(yīng)該考慮選取合適的徑流預(yù)測(cè)模型且對(duì)徑流資料進(jìn)行三性審查,以確保預(yù)測(cè)的準(zhǔn)確程度。選定歷史年徑流資料的頻率分布曲線,根據(jù)年徑流的累計(jì)頻率在頻率曲線上確定豐、平、枯狀態(tài)劃分的臨界值,從而確定各年均徑流量的狀態(tài)。建立歷史徑流集對(duì)和當(dāng)前徑流集對(duì),并與BP神經(jīng)網(wǎng)絡(luò)方法進(jìn)行耦合從而實(shí)現(xiàn)對(duì)徑流的預(yù)測(cè),結(jié)果表明取得的預(yù)測(cè)結(jié)果良好。(2)考慮到集對(duì)分析方法在徑流等級(jí)劃分上存在主觀性,提出模糊化徑流分級(jí)標(biāo)準(zhǔn),并引入年內(nèi)徑流分配的豐枯貢獻(xiàn)權(quán)重因子,計(jì)算出年徑流對(duì)于各個(gè)等級(jí)的綜合隸屬度,并建立綜合隸屬度和理想分級(jí)隸屬度的集合,引進(jìn)模糊數(shù)學(xué)中貼近度的概念,運(yùn)用模糊識(shí)別的方法對(duì)徑流集合中的各元素進(jìn)行分類,通過(guò)比較貼近度的大小進(jìn)行最終判斷。將該分類方法應(yīng)用于年徑流預(yù)測(cè)中,結(jié)果表明所得的預(yù)測(cè)結(jié)果相對(duì)誤差較小,該分類方法準(zhǔn)確、有效。(3)為分析識(shí)別影響城市用水量的相關(guān)影響因子,運(yùn)用灰色關(guān)聯(lián)分析方法計(jì)算各影響因子的灰色關(guān)聯(lián)度,通過(guò)比較灰色關(guān)聯(lián)度的大小確定對(duì)城市用水量影響較大的影響因子,并利用灰色關(guān)聯(lián)度來(lái)計(jì)算各個(gè)影響因子所占的權(quán)重。在建立集對(duì)分析聚類預(yù)測(cè)模型中,分別通過(guò)灰色關(guān)聯(lián)度和層次分析法這兩種方法計(jì)算每個(gè)影響因素的權(quán)重,建立待預(yù)測(cè)樣本與參照系統(tǒng)之間的聯(lián)系度,進(jìn)而預(yù)測(cè)出待預(yù)測(cè)年份的用水量。經(jīng)計(jì)算前者具有較高的精度,是預(yù)測(cè)城市用水量較理想的方法。
[Abstract]:Water is an irreplaceable important resource for human survival and development. Since the beginning of the 21st century, many countries are facing the challenge of water resources crisis. Water shortage, water pollution and flood disasters have seriously hindered the economic and social development of our country and caused a series of social problems. It is of great significance to study the prediction of water resources to realize the sustainable utilization of water resources, to promote the sustainable development of water resources and economic, social and ecological environment, and to alleviate the crisis of water resources in China. The main problem in the process of water resources prediction is how to deal with uncertainty effectively, and the set pair analysis method has obvious advantages in dealing with these problems. Based on this, based on the theory of set pair analysis, the prediction method of water resources system and its application are studied in this paper. The following research results are obtained: (1) there is uncertainty in the classification of runoff levels in the present research. In order to ensure the accuracy of runoff prediction, it is suggested that the appropriate runoff forecasting model should be selected and the runoff data should be examined three ways in order to ensure the accuracy of the prediction. The frequency distribution curve of historical annual runoff data is selected and the critical value of annual runoff annual runoff is determined on the frequency curve according to the cumulative frequency of annual runoff. The historical runoff set pair and the current runoff set pair are established and coupled with BP neural network method to realize runoff prediction. The results show that the predicted results are good. The fuzzy runoff classification standard is put forward, and the weight factor of the annual runoff distribution is introduced to calculate the comprehensive membership degree of the annual runoff to each grade, and to establish the set of the comprehensive membership degree and the ideal grade membership degree. This paper introduces the concept of closeness in fuzzy mathematics, classifies the elements in runoff set by fuzzy recognition method, and makes the final judgment by comparing the size of closeness. The results show that the relative error of the predicted results is small, and the classification method is accurate and effective. It is an effective factor to analyze and identify the influence factors of urban water consumption. The grey correlation degree of each influence factor is calculated by using the method of grey correlation analysis, and the influence factor on urban water consumption is determined by comparing the size of grey correlation degree, and the weight of each influence factor is calculated by using grey correlation degree. In the cluster prediction model of set pair analysis, the weight of each influencing factor is calculated by grey correlation degree and analytic hierarchy process, and the relation between the sample to be predicted and the reference system is established. Furthermore, the water consumption of the year to be forecasted is predicted. The former has higher accuracy and is an ideal method for predicting urban water consumption.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TV213.4

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本文編號(hào)：1797951