本文選題：不確定數(shù)據(jù) + 滑坡��； 參考：《計算機工程》2017年02期

【摘要】：針對滑坡危險性預測中降雨等不確定因素難以獲取,以及有效處理和標準反向傳播算法存在局部極小值和訓練速度慢等問題,為提高滑坡危險性的預測精度,提出一種不確定遺傳神經(jīng)網(wǎng)絡滑坡預測方法�；诟倪M遺傳算法和反向傳播神經(jīng)網(wǎng)絡分類算法,結合滑坡災害預測相關理論,考慮到與滑坡災害密切相關的降雨等不確定因素,給出不確定數(shù)據(jù)分離度的概念,闡述不確定屬性數(shù)據(jù)的處理方法,構建不確定遺傳神經(jīng)網(wǎng)絡,建立滑坡災害預測模型,以延安寶塔區(qū)為例進行驗證。實驗結果顯示,該方法的有效精度和總體精度分別為92.1%和86.7%,驗證了不確定遺傳神經(jīng)網(wǎng)絡算法在滑坡災害預測中的可行性。
[Abstract]:In order to improve the prediction accuracy of landslide risk, it is difficult to obtain uncertain factors such as rainfall in landslide risk prediction, and there are some problems in effective treatment and standard back-propagation algorithm, such as local minimum value and slow training speed. An uncertain genetic neural network landslide prediction method is proposed. Based on improved genetic algorithm and back-propagation neural network classification algorithm, combined with the related theory of landslide disaster prediction, considering the uncertain factors such as rainfall closely related to landslide disaster, the concept of uncertain data separation degree is given. The processing method of uncertain attribute data is expounded, the uncertain genetic neural network is constructed, and the landslide disaster prediction model is established, which is verified by taking Baota area, Yan'an as an example. The experimental results show that the effective accuracy and overall accuracy of the method are 92.1% and 86.7% respectively. The feasibility of the uncertain genetic neural network algorithm in landslide disaster prediction is verified.
【作者單位】： 江西理工大學信息工程學院;江西理工大學資源與環(huán)境工程學院;江西理工大學應用科學學院;
【基金】：國家自然科學基金“基于不確定數(shù)據(jù)挖掘的滑坡區(qū)域地質災害危險性評價方法”(41362015)
【分類號】：P642.22;TP18

【參考文獻】

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本文編號：1866539