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研究生:蔡宗昕
研究生(外文):Tsai, Tzung-Hsin
論文名稱:結合訊號分析與輕量化梯度提升機解析濁水溪沖積扇地下水位之時空特性
論文名稱(外文):Using Signal Processing and Light Gradient Boosting Machine to Estimate the Spatio-temporal Patterns of Groundwater Level in Chuoshui River Alluvial Fan
指導教授:陳憲宗陳憲宗引用關係
指導教授(外文):Chen, Shien-Tsung
口試委員:游景雲孫建平葉信富楊道昌陳憲宗
口試日期:2023-05-19
學位類別:碩士
校院名稱:國立成功大學
系所名稱:水利及海洋工程學系
學門:工程學門
學類:河海工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:182
中文關鍵詞:地下水位訊號分析輕量化梯度提升機氣候變遷
外文關鍵詞:groundwater level forecastingsignal processinglight gradient boosting machineclimate change
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本研究針對濁水溪沖積扇第一含水層之地下水位與相關水文變量(降雨量、抽水量與河川流量)進行小波分析與獨立成分分析,結果顯示訊號分析技術可將變量特徵可視化,解析複雜的地下水系統,並藉由分析各變量與地下水位之交互作用及獨立成分,得知對地下水位有不同程度的影響之因素有:降雨量、地質特性、河川距離、流量大小、抽水標的、抽水量多寡。除地下水系統分析外,本研究使用輕量化梯度提升機來建立區域地下水位之短期預報及長期推估模式,輸入變量為地下水位、降雨量、抽水量、測站標記及月份,短期預報考量未來一個月至三個月的提前預報,結果顯示此模式能夠合理的預報地下水位,未來一個月預報水位之平均相關係數可達0.8、效率係數為0.6;長期推估則模擬氣候變遷情境(SSP2-4.5、SSP5-8.5)下未來短期(2021至2040年)、未來中期(2041至2060年)與未來長期(2081至2100年)之地下水位變動情形,並且與基期(1995至2014年)之模擬地下水位進行比較,了解不同情境下地下水位之時空變化。研究結果顯示,氣候變遷情境下區域地下水位於3至6月下降,於7至10月抬升。本研究以輕量化梯度提升機建立之全域地下水位模式,能快速處理大型數據與類別特徵,合理預報未來短期地下水位之時空變化趨勢,並有效應用於推估氣候變遷下之地下水位變動情勢。
The objective of this study is to estimate the spatio-temporal patterns of groundwater level in Chuoshui River Alluvial Fan. The groundwater level and hydrological variables (rainfall, river flow, and pumping data) were analyzed by signal processing to reveal the relationship between groundwater level and those variables, and the results were combined with light gradient boosting machine to perform short-term prediction and long-term projection of groundwater level in the study area. Results show that the factors that cause groundwater level variations are rainfall, geological properties, distance from station to river, river discharge, and pumping target and volume. The groundwater level model can effectively simulate the groundwater level characteristics of all stations at once, correctly forecast the 1- to 3-month-ahead groundwater levels, and reasonably project the spatio-temporal changes of groundwater level under climate change.
摘要 i
誌謝 viii
目錄 ix
表目錄 xii
圖目錄 xiii
第一章 緒論 1
1.1 研究目的 1
1.2 文獻回顧 2
1.2.1 訊號分析 2
1.2.2 人工智慧應用 5
1.3 本文架構 9
第二章 基本資料介紹 11
2.1 濁水溪沖積扇 11
2.2 地下水位 12
2.3 降雨量 13
2.3.1 短期預報 13
2.3.2 長期推估 15
2.4 河川流量 16
2.5 抽水量 17
2.6 地質鑽探資料 17
2.7 資料補遺方式 18
第三章 研究方法 20
3.1 小波分析 20
3.1.1 連續小波轉換 20
3.1.2 交叉小波轉換 21
3.1.3 小波相關轉換 21
3.1.4 小波分析圖 21
3.2 獨立成分分析 22
3.3 輕量化梯度提升機 26
3.4 評鑑指標 31
第四章 地下水位分析 33
4.1 單站小波分析 33
4.1.1 扇尾:海園 33
4.1.2 扇央:好修 34
4.1.3 扇頂:古坑 37
4.2 多站小波分析 44
4.2.1 地下水位 44
4.2.2 降雨量 46
4.2.3 抽水量 52
4.3 獨立成分分析 53
4.3.1 全域降雨成分 53
4.3.2 全域河川流量成分 57
4.3.3 分區抽水量成分 62
第五章 地下水位短期預報 68
5.1 資料處理 68
5.2 模式率定 68
5.3 模式驗證 72
5.4 獨立成分模式 74
第六章 地下水位長期推估 80
6.1 CanESM5氣候模式下變化情形 80
6.2 MIROC6氣候模式下變化情形 81
6.3 MPI-ESM1-2-LR氣候模式下變化情形 81
6.4 TaiESM1氣候模式下變化情形 81
第七章 結論與建議 91
7.1 結論 91
7.2 建議 92
參考文獻 94
附錄一 101
附錄二 138
附錄三 159
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