本文選題：水質(zhì)評(píng)價(jià) + BP神經(jīng)網(wǎng)絡(luò)　； 參考：《哈爾濱師范大學(xué)》2015年碩士論文

【摘要】：在社會(huì)高度發(fā)達(dá)的今天,經(jīng)濟(jì)發(fā)展的速度越來越快,人類無止境的向自然進(jìn)行索取,肆意的破壞著我們的家園,水環(huán)境的污染首當(dāng)其沖。水環(huán)境質(zhì)量的改善刻不容緩。對(duì)水質(zhì)進(jìn)行評(píng)價(jià)可以得知水環(huán)境質(zhì)量的現(xiàn)狀,為水環(huán)境的治理提供科學(xué)的依據(jù)。作為全國(guó)七大水系之一的松花江,承載著周圍的生活和生產(chǎn)。本研究以松花江哈爾濱段為研究對(duì)象,針對(duì)松花江哈爾濱段的具體情況,在綜合考慮了影響松花江水質(zhì)的各種自然及人文因素的基礎(chǔ)上,結(jié)合重要性原則本文選取了包括化學(xué)需氧量、五日生化需氧量以及氨氮在內(nèi)的18個(gè)水質(zhì)監(jiān)測(cè)指標(biāo)作為松花江水質(zhì)評(píng)價(jià)的基礎(chǔ)指標(biāo)。通過主成分分析法對(duì)研究指標(biāo)進(jìn)行篩選,最終選取了化學(xué)需氧量、五日生化需氧量、高錳酸鹽指數(shù)、氨氮、總磷和石油類作為BP神經(jīng)網(wǎng)絡(luò)評(píng)價(jià)水質(zhì)的水質(zhì)指標(biāo)。研究中采用的水質(zhì)數(shù)據(jù)為2009-2013年朱順屯、阿什河口內(nèi)、阿什河口下、呼蘭河口內(nèi)、呼蘭河口下和大頂子山6個(gè)斷面指標(biāo)的月度監(jiān)測(cè)值。分為按年綜合評(píng)價(jià)和按枯水期、平水期和豐水期時(shí)期來進(jìn)行評(píng)價(jià)。采用的水質(zhì)評(píng)價(jià)方法為主成分分析法和BP神經(jīng)網(wǎng)絡(luò)的方法,通過主成分分析法對(duì)各斷面的主成分值進(jìn)行排名,得出各斷面的污染先后順序,其次選取污染指標(biāo)中的主成分,為BP神經(jīng)網(wǎng)絡(luò)模擬水質(zhì)提供指標(biāo)依據(jù)。通過訓(xùn)練BP神經(jīng)網(wǎng)絡(luò),得出最優(yōu)的評(píng)價(jià)模型,通過模型分別對(duì)各個(gè)時(shí)期的斷面進(jìn)行評(píng)價(jià),對(duì)各斷面的污染等級(jí)進(jìn)行劃分,得出各斷面的水質(zhì)分類等級(jí)。本文主成分分析法選用的軟件為SPSS22.0,BP神經(jīng)網(wǎng)絡(luò)法選用的是MATLAB2010a。評(píng)價(jià)結(jié)果表明:六個(gè)斷面中水質(zhì)最差的為阿什河口內(nèi)斷面,五年內(nèi)排名均在最后一名,水質(zhì)均為Ⅴ類水質(zhì),水質(zhì)最好的為朱順屯斷面,為Ⅱ-Ⅲ類水質(zhì),其余四個(gè)斷面水質(zhì)均較好,基本達(dá)到規(guī)劃的水質(zhì)要求。通過兩種方法來評(píng)價(jià)松花江哈爾濱段的水質(zhì)情況,為改善和治理水質(zhì)提供科學(xué)依據(jù)。
[Abstract]:In the highly developed society today, the speed of economic development is getting faster and faster. Human beings are taking endless demands from nature, wantonly destroying our homes, and the pollution of water environment bears the brunt. It is urgent to improve the quality of water environment. To evaluate the water quality, we can know the present situation of the water environment quality, and provide scientific basis for the water environment management. As one of the seven major water systems in the country, the Songhua River carries the life and production around it. This study takes Harbin section of Songhua River as the research object, according to the concrete situation of Harbin section of Songhua River, on the basis of synthetically considering all kinds of natural and human factors that affect the water quality of Songhua River. According to the principle of importance, 18 water quality monitoring indexes, including chemical oxygen demand, five days biochemical oxygen demand and ammonia nitrogen, were selected as the basic indexes for water quality evaluation of Songhua River. The chemical oxygen demand, five days biochemical oxygen demand, permanganate index, ammonia nitrogen, total phosphorus and petroleum were selected as the water quality indexes of BP neural network. The water quality data used in the study are monthly monitoring values of 6 cross section indexes in Zhushuntun, Ashi Estuary, Hulan River Estuary, Hulan River Estuary and Dadingzi Mountain from 2009 to 2013. It can be divided into annual comprehensive evaluation and dry, plain and abundant water periods. The methods of water quality evaluation are principal component analysis and BP neural network. The principal component value of each section is ranked by principal component analysis, the pollution sequence of each section is obtained, and the principal component of pollution index is selected. It provides index basis for BP neural network to simulate water quality. Through training BP neural network, the optimal evaluation model is obtained. The section of each period is evaluated by the model, the pollution grade of each section is divided, and the water quality classification grade of each section is obtained. In this paper, the software of principal component analysis is SPSS 22.0 BP neural network method and MATLAB2010a. The results show that the worst water quality of the six sections is the Ashe estuary section, which ranks last in five years. The water quality is category V, the best water quality is the Zhushuntun section, and the water quality is class 鈪，

本文編號(hào)：1919825