本文關(guān)鍵詞：基于空間信息格網(wǎng)和BP神經(jīng)網(wǎng)絡(luò)的洪災(zāi)損失評估研究　出處：《江西理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 空間信息格網(wǎng) BP神經(jīng)網(wǎng)絡(luò) 評估模型 洪災(zāi)損失評估系統(tǒng)

【摘要】：洪澇災(zāi)害是我國最嚴(yán)重的自然災(zāi)害之一,它具有發(fā)生頻次高、破壞性大以及影響范圍廣等特點。洪澇災(zāi)害給我國帶來的人員傷亡和經(jīng)濟損失,已經(jīng)嚴(yán)重地制約了我國社會經(jīng)濟的可持續(xù)發(fā)展。對洪災(zāi)經(jīng)濟損失的有效評估不僅能夠?qū)Ψ篮闇p災(zāi)行為與防洪工程所發(fā)揮出的效益進行準(zhǔn)確評價,而且還能為抗洪救災(zāi)提供重要的決策依據(jù)。但是當(dāng)前洪災(zāi)損失評估方法存在計算量大、操作繁瑣以及評估精度低等問題,為此開展了基于空間信息格網(wǎng)和BP神經(jīng)網(wǎng)絡(luò)的洪災(zāi)損失評估研究,本文的主要工作內(nèi)容和創(chuàng)新點如下:(1)使用空間信息格網(wǎng)技術(shù)應(yīng)用在洪災(zāi)損失評估中,首先將受災(zāi)區(qū)域劃分成洪水特性格網(wǎng)和社會經(jīng)濟展布格網(wǎng);然后使用疊加分析方法將兩者進行疊加分析生成進行洪災(zāi)損失評估的空間信息格網(wǎng);最后應(yīng)用C#和Arc Engine實現(xiàn)了基于空間信息格網(wǎng)的洪災(zāi)損失評估系統(tǒng)。(2)使用BP神經(jīng)網(wǎng)絡(luò)技術(shù)應(yīng)用在洪災(zāi)損失評估中,首先收集好洪災(zāi)損失評估原始數(shù)據(jù);然后提取洪災(zāi)評估影響因子;其次構(gòu)建BP神經(jīng)網(wǎng)絡(luò),并在此基礎(chǔ)上構(gòu)建洪災(zāi)損失評估模型;最后使用編程手段實現(xiàn)了基于BP神經(jīng)網(wǎng)絡(luò)的洪災(zāi)損失評估系統(tǒng)。(3)利用空間信息格網(wǎng)在淹沒水深提取以及淹沒面積統(tǒng)計的特點,結(jié)合BP神經(jīng)網(wǎng)絡(luò)能夠?qū)闉?zāi)樣本數(shù)據(jù)逐一進行歸一化、訓(xùn)練、測試并得出預(yù)測值的優(yōu)勢,建立基于空間信息格網(wǎng)與BP神經(jīng)網(wǎng)絡(luò)的洪災(zāi)損失評估模型。(4)使用Arc GIS Engine開發(fā)引擎基于C#編程語言,并在處理好樣本數(shù)據(jù)的基礎(chǔ)上實現(xiàn)基于空間信息格網(wǎng)和BP神經(jīng)網(wǎng)絡(luò)的洪災(zāi)損失評估系統(tǒng)。應(yīng)用開發(fā)好的洪災(zāi)損失評估系統(tǒng)針對鄱陽湖區(qū)某縣2013年的洪災(zāi)進行了洪災(zāi)損失評估,得出的評估結(jié)果與當(dāng)年實際經(jīng)濟損失結(jié)果接近,誤差率較小。
[Abstract]:Flood and waterlogging is one of the most serious natural disasters in China, which has the characteristics of high frequency, great destruction and wide range of influence. The flood and waterlogging disaster brings casualties and economic losses to our country. It has seriously restricted the sustainable development of China's social economy. The effective evaluation of flood economic losses can not only accurately evaluate the flood prevention and mitigation behavior and the benefits of flood control projects. It can also provide an important decision basis for flood control and disaster relief. However, the current flood loss assessment method has many problems, such as large amount of calculation, cumbersome operation and low evaluation accuracy. For this reason, the research on flood damage assessment based on spatial information grid and BP neural network is carried out. The main contents and innovations of this paper are as follows: 1) the use of spatial information grid technology in flood disaster loss assessment. First, the affected area is divided into flood characteristic grid and socio-economic grid; Then the superposition analysis method is used to generate the spatial information grid for flood damage assessment. Finally, the flood damage assessment system based on spatial information grid is implemented by using C # and Arc Engine. BP neural network technology is used in flood damage assessment. First, collect the original data of flood damage assessment; And then extract the impact factors of flood assessment; Secondly, BP neural network is constructed, and on this basis, flood loss assessment model is constructed. Finally, a flood loss assessment system based on BP neural network is realized by programming method. It uses spatial information grid to extract the depth of submerged water and to calculate the submergence area. The BP neural network can normalize, train, test and get the predictive value of flood data one by one. A flood damage assessment model based on spatial information grid and BP neural network is established. (4) Arc GIS Engine development engine is developed based on C # programming language. The flood disaster loss assessment system based on spatial information grid and BP neural network is realized on the basis of processing the sample data. The application of the developed flood damage assessment system is aimed at the flood disaster in 2013 in a county in Poyang Lake region. A flood damage assessment was carried out. The result of evaluation is close to that of actual economic loss, and the error rate is small.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P426.616;P208;TP18

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