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戴卫军
蔡智明
數據科學學院
數據科學博士學位課程(中文學制)
博士
2020
基於集成學習的沿海城市水浸預測研究 : 以澳門爲例
Research on Forecasting of Coastal Urban Flood Based on Ensemble Learning: A Case Study in Macau
水浸預測 ; 水文時間序列 ; 集中學習 ; 貝葉斯模型組合
Flooding prediction ; Hydrological time series ; Ensemble learning ; Bayesian model combination
沿海城市受到氣候變化和人類活動的潛在影響，水浸事件變得更加頻繁和激烈。挖掘水文時序及致災因數資料的豐富資訊，運用機器學習方法預測城市水浸過程，為城市管理部門提供可靠決策資訊，具有重要的理論意義和應用價值。主要成果如下：
（1）從城市生態系統的視角，分析了城市系統與水浸災害系統構成，總結水浸災害系統具有人類因素作用顯著、時空差異性、動態演化性的特徵。沿海城市具有典型的地域環境特徵，水浸災害為水澇型和風暴潮型的複合，水浸致災因數包括颱風路徑、風暴潮、潮汐、降雨與風速、地表徑流、城市地形與高程等。構建了澳門水浸數據集，為資料驅動模型提供研究基礎。
（2）採用長條圖分佈分析、變量相關性分析、變量重要性統計方法，分析了澳門水浸的年內季節變化特徵、水浸分佈趨勢、水浸與水浸因數關係。研究表明，澳門水浸集中發生在每年的8月到10月，並且與進入澳門800Km範圍的颱風事件緊密相關。線性相關性無法很好的表現水浸和水浸因數的非線性關係，變量重要性統計能較好地表現單個颱風事件的水浸因數貢獻率。
（3）針對水浸預測模型輸入因數難以選擇的問題，設計了多個三層靜態BP神經網路模型，引入ANPSFS方法，進行了一系列探索性實驗，通過實驗獲得構建模型的相關參數。針對特定水浸數據集，確定了預測模型的35個最佳輸入因數，最佳訓練樣本數為1分鐘間隔資料，最佳預見期為60分鐘。
（4）設計了基於貝葉斯模型組合的集成學習方法（BMC-EL）進行60分預見期的水浸預測。首先，引入洪水強度等級分類和K折交叉校驗，將訓練集生成多個訓練子集，實現均勻採樣和增加子集的多樣性。其次，將BP神經網路和隨機森林作為基學習器建立預測模型，導入訓練子集進行訓練。最後，依據基學習器在驗證集上的預測性能，制定貝葉斯模型組合策略，將測試集上的預測值集成輸出。研究表明，與線性回歸、線性支援向量機、二次支援向量機、BP神經網路、隨機森林相比，BMC-EL為最優模型，最大水深預測誤差為0.2m，顯著優於其他模型。使用水浸等級上的預測準確率來評價模型可靠性，BMC-EL可靠性最低為等級2的 65.31%，其餘等級的可靠性都在80%以上。BMC-EL模型預測表現非常穩定，預測輸出可以為城市管理者發佈水浸警報提供有效指導。
（1）從城市生態系統的視角，分析了城市系統與水浸災害系統構成，總結水浸災害系統具有人類因素作用顯著、時空差異性、動態演化性的特徵。沿海城市具有典型的地域環境特徵，水浸災害為水澇型和風暴潮型的複合，水浸致災因數包括颱風路徑、風暴潮、潮汐、降雨與風速、地表徑流、城市地形與高程等。構建了澳門水浸數據集，為資料驅動模型提供研究基礎。
（2）採用長條圖分佈分析、變量相關性分析、變量重要性統計方法，分析了澳門水浸的年內季節變化特徵、水浸分佈趨勢、水浸與水浸因數關係。研究表明，澳門水浸集中發生在每年的8月到10月，並且與進入澳門800Km範圍的颱風事件緊密相關。線性相關性無法很好的表現水浸和水浸因數的非線性關係，變量重要性統計能較好地表現單個颱風事件的水浸因數貢獻率。
（3）針對水浸預測模型輸入因數難以選擇的問題，設計了多個三層靜態BP神經網路模型，引入ANPSFS方法，進行了一系列探索性實驗，通過實驗獲得構建模型的相關參數。針對特定水浸數據集，確定了預測模型的35個最佳輸入因數，最佳訓練樣本數為1分鐘間隔資料，最佳預見期為60分鐘。
（4）設計了基於貝葉斯模型組合的集成學習方法（BMC-EL）進行60分預見期的水浸預測。首先，引入洪水強度等級分類和K折交叉校驗，將訓練集生成多個訓練子集，實現均勻採樣和增加子集的多樣性。其次，將BP神經網路和隨機森林作為基學習器建立預測模型，導入訓練子集進行訓練。最後，依據基學習器在驗證集上的預測性能，制定貝葉斯模型組合策略，將測試集上的預測值集成輸出。研究表明，與線性回歸、線性支援向量機、二次支援向量機、BP神經網路、隨機森林相比，BMC-EL為最優模型，最大水深預測誤差為0.2m，顯著優於其他模型。使用水浸等級上的預測準確率來評價模型可靠性，BMC-EL可靠性最低為等級2的 65.31%，其餘等級的可靠性都在80%以上。BMC-EL模型預測表現非常穩定，預測輸出可以為城市管理者發佈水浸警報提供有效指導。
Coastal cities are potentially affected by climate change and human activities, and flooding events have become more frequent and intense. Excavating the rich information of hydrological time series and hazard factor data, using machine learning methods to predict the process of urban flooding, and providing reliable decision-making information for urban management departments, has important theoretical significance and application value. The main results are as follows:
(1) From the perspective of the urban ecosystem, the composition of the urban system and the flooding disaster system is analyzed, and it is concluded that the flooding disaster system has the characteristics of significant human factors, temporal and spatial differences, and dynamic evolution. Coastal cities have typical regional environmental characteristics. Flooding disasters are a combination of flooding and storm surge types. Flooding hazards include typhoon paths, storm surges, tides, rainfall and wind speed, surface runoff, urban topography and elevation. The Macao flooding data set was constructed to provide a research foundation for the data-driven model.
(2) Using histogram distribution analysis, variable correlation analysis, and variable importance statistical methods, the seasonal variation characteristics, flooding distribution trends, and the relationship between flooding and flooding factors in Macau during the year were analyzed. Studies have shown that flooding in Macau occurs intensively from August to October each year, and is closely related to the typhoon event that enters Macau's 800km range. Linear correlation cannot well represent the non-linear relationship between flooding and flooding factors, and variable importance statistics can better represent the contribution rate of flooding factors of a single typhoon event.
(3) Aiming at the problem that it is difficult to select the input factors of the flood prediction model, a number of three-layer static BP neural network models are designed, the ANPSFS method is introduced, a series of exploratory experiments are carried out, and the relevant parameters of the constructed model are obtained through experiments. For a specific flooding data set, 35 optimal input factors of the prediction model are determined, the optimal number of training samples is 1-minute interval data, and the optimal forecast period is 60 minutes.
(4) Designed an integrated learning method based on the combination of Bayesian model (BMC-EL) to predict flooding with a 60-minute forecast period. First, the flood intensity classification and K-fold cross-checking are introduced to generate multiple training subsets from the training set to achieve uniform sampling and increase the diversity of the subsets. Secondly, the BP neural network and random forest are used as the base learner to build the prediction model, and then import the training subset for training. Finally, according to the prediction performance of the base learner on the verification set, a Bayesian model combination strategy is formulated, and the predicted values on the test set are integrated and output. Research shows that compared with linear regression, linear support vector machine, quadratic support vector machine, BP neural network, and random forest, BMC-EL is the optimal model with a maximum water depth prediction error of 0.2m, which is significantly better than other models. The prediction accuracy rate on the flooding level is used to evaluate the reliability of the model. The reliability of BMC-EL is at least 65.31% of level 2, and the reliability of the other levels is above 80%. The prediction performance of the BMC-EL model is very stable, and the prediction output can provide effective guidance for city managers to issue flood warnings.
(1) From the perspective of the urban ecosystem, the composition of the urban system and the flooding disaster system is analyzed, and it is concluded that the flooding disaster system has the characteristics of significant human factors, temporal and spatial differences, and dynamic evolution. Coastal cities have typical regional environmental characteristics. Flooding disasters are a combination of flooding and storm surge types. Flooding hazards include typhoon paths, storm surges, tides, rainfall and wind speed, surface runoff, urban topography and elevation. The Macao flooding data set was constructed to provide a research foundation for the data-driven model.
(2) Using histogram distribution analysis, variable correlation analysis, and variable importance statistical methods, the seasonal variation characteristics, flooding distribution trends, and the relationship between flooding and flooding factors in Macau during the year were analyzed. Studies have shown that flooding in Macau occurs intensively from August to October each year, and is closely related to the typhoon event that enters Macau's 800km range. Linear correlation cannot well represent the non-linear relationship between flooding and flooding factors, and variable importance statistics can better represent the contribution rate of flooding factors of a single typhoon event.
(3) Aiming at the problem that it is difficult to select the input factors of the flood prediction model, a number of three-layer static BP neural network models are designed, the ANPSFS method is introduced, a series of exploratory experiments are carried out, and the relevant parameters of the constructed model are obtained through experiments. For a specific flooding data set, 35 optimal input factors of the prediction model are determined, the optimal number of training samples is 1-minute interval data, and the optimal forecast period is 60 minutes.
(4) Designed an integrated learning method based on the combination of Bayesian model (BMC-EL) to predict flooding with a 60-minute forecast period. First, the flood intensity classification and K-fold cross-checking are introduced to generate multiple training subsets from the training set to achieve uniform sampling and increase the diversity of the subsets. Secondly, the BP neural network and random forest are used as the base learner to build the prediction model, and then import the training subset for training. Finally, according to the prediction performance of the base learner on the verification set, a Bayesian model combination strategy is formulated, and the predicted values on the test set are integrated and output. Research shows that compared with linear regression, linear support vector machine, quadratic support vector machine, BP neural network, and random forest, BMC-EL is the optimal model with a maximum water depth prediction error of 0.2m, which is significantly better than other models. The prediction accuracy rate on the flooding level is used to evaluate the reliability of the model. The reliability of BMC-EL is at least 65.31% of level 2, and the reliability of the other levels is above 80%. The prediction performance of the BMC-EL model is very stable, and the prediction output can provide effective guidance for city managers to issue flood warnings.
2021
中文
162
致 謝 I
摘要 III
Abstract V
目 錄 VII
圖目錄 X
表目錄 XII
第一章 緒論 1
1.1 研究背景和意義 1
1.1.1 變化環境下城市水浸災害加劇 1
1.1.2 大數據及AI技術推進水浸研究 4
1.2 研究問題 7
1.3 研究目的 8
1.4 研究方法 9
1.5 研究技術路線 10
1.6 研究創新 10
1.7 章節安排 12
第二章 文獻綜述 15
2.1 城市水浸 15
2.1.1 城市 15
2.1.2 沿海城市 16
2.1.3 城市水浸災害 18
2.1.3.1 城市水浸災害類型 18
2.1.3.2 城市水浸災害系統構成 19
2.1.3.3 城市水浸災害系統特徵 23
2.1.3.4 沿海城市水浸預測因子 26
2.2 基於城市雨洪管理模型的水浸預測 27
2.2.1 城市雨洪管理模型 27
2.2.1.1 InfoWorks CS模型 28
2.2.1.2 SWMM 模型 28
2.2.2 基於城市雨洪管理模型的水浸預測 29
2.3 基於時間序列分析的水文預測 31
2.3.1 時間序列及其預測 31
2.3.2 水文時間序列 32
2.3.3 基於時間序列預測模型的水文預測 33
2.3.4 基於神經網路的水文預報 34
2.3.5 基於改進機器學習模型的水文預報 36
2.4 沿海城市的水浸預測 37
2.4.1 IoT系統在城市水浸的應用 37
2.4.2 沿海城市的水浸預測 40
2.5 研究述評 41
2.6 本章小結 42
第三章 研究方法 44
3.1 數據預處理方法 44
3.1.1 時間序列數據丟失分類 44
3.1.2 處理長時丟失數據 45
3.1.3 填補一維暫態丟失數據 47
3.1.4 填補多維短時丟失數據 48
3.2 澳門水浸數據預處理 51
3.2.1 澳門水浸概況 51
3.2.2 澳門水浸數據 53
3.2.2.1 水浸高度數據 53
3.2.2.2 城市氣象數據 56
3.2.2.3 颱風最佳路徑數據 56
3.2.2.4 近海天文潮數據 60
3.2.2.5 近海風暴潮數據 63
3.3 預測方法 63
3.3.1 預測理論與方法 63
3.3.2 神經網路 64
3.3.3 集成學習 66
3.3.3.1 Boosting 66
3.3.3.2 Bagging 68
3.3.3.3 結合策略 69
3.3.4 預測性能評價指標 72
3.3.4.1 確定性係數 72
3.3.4.2 均方误差 72
3.3.4.3 平均絕對誤差 72
3.3.4.4 均方根誤差 73
3.4 本章小結 73
第四章 研究結果 74
4.1 數據構成與分析 74
4.1.1 澳門水浸數據集的構成 74
4.1.2 澳門典型水浸數據 77
4.1.2.1 “天鴿”水浸數據 77
4.1.2.1 “山竹”水浸數據 78
4.1.3 澳門水浸數據分析 80
4.1.3.1 水浸數據的特徵 80
4.1.3.2 水浸因子數據的特徵 82
4.1.3.3 水浸與水浸因子的關係 84
4.2 BP神經網路預測 88
4.2.1 BP神經網路模型 88
4.2.2 討論與分析 91
4.2.2.1 水浸因子敏感性分析 91
4.2.2.2 訓練樣本數敏感性分析 100
4.2.2.2 不同預見期的預測分析 106
4.3 貝葉斯模型組合的集成學習預測 110
4.3.1 集成學習方法 111
4.3.1.1 模型的構建 111
4.3.1.2 BP神經網路基學習器 113
4.3.1.3 隨機森林基學習器 114
4.3.1.4 貝葉斯模型組合策略 116
4.3.1.5 參數設置 118
4.3.2 討論與分析 120
4.3.2.1 訓練子集的多樣性 120
4.3.2.2 預測精度 123
4.3.2.3 預測誤差 125
4.3.2.4 預測可靠性 128
4.3.2.5 消融實驗 133
4.4 本章小結 133
第五章 結論與展望 135
5.1 結論 135
5.2 局限性 138
5.3 研究展望 139
參考文獻 141
作者簡歷(讀博期間成果) 161
摘要 III
Abstract V
目 錄 VII
圖目錄 X
表目錄 XII
第一章 緒論 1
1.1 研究背景和意義 1
1.1.1 變化環境下城市水浸災害加劇 1
1.1.2 大數據及AI技術推進水浸研究 4
1.2 研究問題 7
1.3 研究目的 8
1.4 研究方法 9
1.5 研究技術路線 10
1.6 研究創新 10
1.7 章節安排 12
第二章 文獻綜述 15
2.1 城市水浸 15
2.1.1 城市 15
2.1.2 沿海城市 16
2.1.3 城市水浸災害 18
2.1.3.1 城市水浸災害類型 18
2.1.3.2 城市水浸災害系統構成 19
2.1.3.3 城市水浸災害系統特徵 23
2.1.3.4 沿海城市水浸預測因子 26
2.2 基於城市雨洪管理模型的水浸預測 27
2.2.1 城市雨洪管理模型 27
2.2.1.1 InfoWorks CS模型 28
2.2.1.2 SWMM 模型 28
2.2.2 基於城市雨洪管理模型的水浸預測 29
2.3 基於時間序列分析的水文預測 31
2.3.1 時間序列及其預測 31
2.3.2 水文時間序列 32
2.3.3 基於時間序列預測模型的水文預測 33
2.3.4 基於神經網路的水文預報 34
2.3.5 基於改進機器學習模型的水文預報 36
2.4 沿海城市的水浸預測 37
2.4.1 IoT系統在城市水浸的應用 37
2.4.2 沿海城市的水浸預測 40
2.5 研究述評 41
2.6 本章小結 42
第三章 研究方法 44
3.1 數據預處理方法 44
3.1.1 時間序列數據丟失分類 44
3.1.2 處理長時丟失數據 45
3.1.3 填補一維暫態丟失數據 47
3.1.4 填補多維短時丟失數據 48
3.2 澳門水浸數據預處理 51
3.2.1 澳門水浸概況 51
3.2.2 澳門水浸數據 53
3.2.2.1 水浸高度數據 53
3.2.2.2 城市氣象數據 56
3.2.2.3 颱風最佳路徑數據 56
3.2.2.4 近海天文潮數據 60
3.2.2.5 近海風暴潮數據 63
3.3 預測方法 63
3.3.1 預測理論與方法 63
3.3.2 神經網路 64
3.3.3 集成學習 66
3.3.3.1 Boosting 66
3.3.3.2 Bagging 68
3.3.3.3 結合策略 69
3.3.4 預測性能評價指標 72
3.3.4.1 確定性係數 72
3.3.4.2 均方误差 72
3.3.4.3 平均絕對誤差 72
3.3.4.4 均方根誤差 73
3.4 本章小結 73
第四章 研究結果 74
4.1 數據構成與分析 74
4.1.1 澳門水浸數據集的構成 74
4.1.2 澳門典型水浸數據 77
4.1.2.1 “天鴿”水浸數據 77
4.1.2.1 “山竹”水浸數據 78
4.1.3 澳門水浸數據分析 80
4.1.3.1 水浸數據的特徵 80
4.1.3.2 水浸因子數據的特徵 82
4.1.3.3 水浸與水浸因子的關係 84
4.2 BP神經網路預測 88
4.2.1 BP神經網路模型 88
4.2.2 討論與分析 91
4.2.2.1 水浸因子敏感性分析 91
4.2.2.2 訓練樣本數敏感性分析 100
4.2.2.2 不同預見期的預測分析 106
4.3 貝葉斯模型組合的集成學習預測 110
4.3.1 集成學習方法 111
4.3.1.1 模型的構建 111
4.3.1.2 BP神經網路基學習器 113
4.3.1.3 隨機森林基學習器 114
4.3.1.4 貝葉斯模型組合策略 116
4.3.1.5 參數設置 118
4.3.2 討論與分析 120
4.3.2.1 訓練子集的多樣性 120
4.3.2.2 預測精度 123
4.3.2.3 預測誤差 125
4.3.2.4 預測可靠性 128
4.3.2.5 消融實驗 133
4.4 本章小結 133
第五章 結論與展望 135
5.1 結論 135
5.2 局限性 138
5.3 研究展望 139
參考文獻 141
作者簡歷(讀博期間成果) 161
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