桃園市列管農地重金屬再汙染預測模型之研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：38

、訪客IP：18.118.9.146

姓名

葉明忠(Ming-Chung Yeh) 查詢紙本館藏

畢業系所

土木系營建管理碩士班

論文名稱

桃園市列管農地重金屬再汙染預測模型之研究
(Study on Prediction Model for Heavy Metal Recontamination in Regulated Farmland in Taoyuan City)

相關論文

★ 運用深度神經網路建立H型鋼構件自動辨識系統之研究	★ 運用關聯法則探討協力廠商對營造廠報價行為之研究
★ 探討影響台灣工程顧問公司落實法律遵循反貪腐關鍵因素之研究	★ 以分包商角度探討對營造廠報價行為策略之研究
★ 運用SOMCM分群演算法開發設計雲端智慧平台之營運維護產業介面-以桃園市某園區為例	★ 專案管理在履約爭議處理機制之比較與研析
★ H型鋼構件智慧塗裝路徑優化研究	★ 以資料包絡分析法評估大型統包營造廠之經營績效-以上市櫃公司為例
★ 運用組織特徵映射圖動作軌跡相似度測量法探索預鑄工項生產效率與資源規劃之研究	★ 預鑄專案成本估算策略之研究
★ 新建工程建造執照查核缺失要項之探討--以台北市為例	★ 灰關聯分析探討古蹟與歷史建築再利用之研究
★ 營造工地管理人資量化與預測	★ 公共建設專案現金管理與控制之研究
★ 營建業ERP整合PDA模型之研究	★ 水庫營運效益評估之研究-以石門水庫為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

桃園市的土地污染面積在全國排名第二。多年來，雖然政府積極地整治大部分受污染的土地，但土地的特性在不斷變化，仍然容易再次受到污染的威脅。然而，目前尚無專門針對桃園市列管農地重金屬再污染的預測方法。因此，本研究以鎘、銅和鋅這三種重金屬為主，利用Python建立了一個基於土壤監測濃度變化和土壤重金屬濃度增量的隨機森林模型。通過文獻回顧確定模型的相關參數，並收集近18年（2004-2021年）的1555組桃園市列管農地數據，應用了四種常見的演算法進行預測，其中隨機森林表現最佳，準確率為75.76%，誤差率為0.05%，顯示出模型具有一定的可靠性。此外，提出了模型的潛在應用價值，期望未來相關單位能夠利用此模型預測潛在的再污染地點，並由環保單位針對性地部署監測和控制措施，從而避免不必要的廣泛測試，以節省時間、人力和財力等資源。

摘要(英)

Taoyuan City has the second largest area of land pollution in the country. Despite the government′s active efforts over the years to remediate most of the contaminated land, the characteristics of the land continue to change, and farmland remains susceptible to potential recontamination. Currently, there is no dedicated method for predicting heavy metal recontamination specifically for Taoyuan City′s regulated farmland. Therefore, this study focuses on cadmium, copper, and zinc, three heavy metals, to establish a random forest model using Python, based on changes in soil monitoring concentrations and soil heavy metal concentration increments. A comprehensive literature review indicates the factors, followed by data collection which involves 1,555 datasets in recent 18 years (2004-2021). There are four common prediction methods applied and random forest performed the best at an accuracy of 75.76% with 0.05% of error, demonstrating a certain level of reliability. Furthermore, potential applications of the model are proposed, with the hope that relevant agencies in the future can use the constructed model to predict potential recontamination sites and enable targeted deployment of monitoring and control measures by environmental protection units, thereby avoiding unnecessary extensive testing and conserving resources such as time, manpower, and finances.

關鍵字(中)

★ 預測
★ 重金屬
★ 再汙染
★ 農地
★ 汙染

關鍵字(英)

★ Prediction
★ Heavy Metal
★ Recontamination
★ Farmland
★ Pollution

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究問題 2
1.3 研究目的 2
1.4 研究範圍與限制 2
1.5 研究流程 3
第二章文獻回顧 5
2.1 土地汙染 5
2.2 汙染預測相關文獻 8
2.3 資料探勘 11
2.3.1資料探勘定義 11
2.3.2資料探勘技術種類 12
2.3.3分類 12
2.3.4回歸分析 17
2.3.5分群分析 17
2.3.6關聯法則 19
2.4 小結 23
第三章資料蒐集與處理 24
3.1 資料蒐集 24
3.1.1 資料來源 24
3.1.2桃園市內列管農地污染資料組成與分析 24
3.2 資料前處理與模型建構 29
3.2.1資料前處理 29
3.2.2模型建構 29
第四章模型預測結果 32
4.1 土壤監測濃度變化情形之預測結果 32
4.2 土壤重金屬濃度增量之預測結果 35
4.3 模型應用 37
4.4 預測結果討論 38
第五章結論與建議 39
5.1 結論 39
5.2 建議 40
參考文獻 41

參考文獻

[1] P. Wang et al., "Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants," Science of the Total Environment, vol. 693, p. 133440, 2019.
[2] T. H. Aldhyani, M. Al-Yaari, H. Alkahtani, and M. Maashi, "Water quality prediction using artificial intelligence algorithms," Applied Bionics and Biomechanics, vol. 2020, 2020.
[3] H. Lu and X. Ma, "Hybrid decision tree-based machine learning models for short-term water quality prediction," Chemosphere, vol. 249, p. 126169, 2020.
[4] J. Yang et al., "Discriminative algorithm approach to forecast Cd threshold exceedance probability for rice grain based on soil characteristics," Environmental Pollution, vol. 261, p. 114211, 2020.
[5] U. Ahmed, R. Mumtaz, H. Anwar, A. A. Shah, R. Irfan, and J. García-Nieto, "Efficient water quality prediction using supervised machine learning," Water, vol. 11, no. 11, p. 2210, 2019.
[6] J. Yang et al., "Brief introduction of medical database and data mining technology in big data era," Journal of Evidence‐Based Medicine, vol. 13, no. 1, pp. 57-69, 2020.
[7] P. Huang, C. Wen, L. Fu, Q. Peng, and Y. Tang, "A deep learning approach for multi-attribute data： A study of train delay prediction in railway systems," Information Sciences, vol. 516, pp. 234-253, 2020.
[8] B. Molina-Coronado, U. Mori, A. Mendiburu, and J. Miguel-Alonso, "Survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process," IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2451-2479, 2020.
[9] S. Laxman and P. S. Sastry, "A survey of temporal data mining," Sadhana, vol. 31, pp. 173-198, 2006.
[10] A. Aleem and M. M. Gore, "Educational data mining methods： A survey," in 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT), 2020： IEEE, pp. 182-188.
[11] M. Mittal, L. M. Goyal, D. J. Hemanth, and J. K. Sethi, "Clustering approaches for high‐dimensional databases： A review," Wiley Interdisciplinary Reviews： Data Mining and Knowledge Discovery, vol. 9, no. 3, p. e1300, 2019.
[12] G. Kesavaraj and S. Sukumaran, "A study on classification techniques in data mining," in 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), 2013： IEEE, pp. 1-7.
[13] B. Charbuty and A. Abdulazeez, "Classification based on decision tree algorithm for machine learning," Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20-28, 2021.
[14] M. Schonlau and R. Y. Zou, "The random forest algorithm for statistical learning," The Stata Journal, vol. 20, no. 1, pp. 3-29, 2020.
[15] J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, "A comparison of random forest variable selection methods for classification prediction modeling," Expert systems with applications, vol. 134, pp. 93-101, 2019.
[16] D. Umar Sidiq, S. M. Aaqib, and R. A. Khan, "Diagnosis of various thyroid ailments using data mining classification techniques," Int J Sci Res Coput Sci Inf Technol, vol. 5, pp. 131-6, 2019.
[17] J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, "A comprehensive survey on support vector machine classification： Applications, challenges and trends," Neurocomputing, vol. 408, pp. 189-215, 2020.
[18] L. A. Demidova, "Two-stage hybrid data classifiers based on SVM and kNN algorithms," Symmetry, vol. 13, no. 4, p. 615, 2021.
[19] S. Ghosh, A. Dasgupta, and A. Swetapadma, "A study on support vector machine based linear and non-linear pattern classification," in 2019 International Conference on Intelligent Sustainable Systems (ICISS), 2019： IEEE, pp. 24-28.
[20] L. Verma, S. Srivastava, and P. Negi, "A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data," Journal of medical systems, vol. 40, pp. 1-7, 2016.
[21] A. Dogan and D. Birant, "Machine learning and data mining in manufacturing," Expert Systems with Applications, vol. 166, p. 114060, 2021.
[22] P. Garikapati, K. Balamurugan, T. Latchoumi, and R. Malkapuram, "A cluster-profile comparative study on machining AlSi7/63% of SiC hybrid composite using agglomerative hierarchical clustering and K-means," Silicon, vol. 13, no. 4, pp. 961-972, 2021.
[23] J. MacQueen, "Some methods for classiﬁcation and analysis of multivariate observations," in Proc. 5th Berkeley Symposium on Math., Stat., and Prob, 1965, p. 281.
[24] S. F. Hussain and M. Haris, "A k-means based co-clustering (kCC) algorithm for sparse, high dimensional data," Expert Systems with Applications, vol. 118, pp. 20-34, 2019.
[25] E. Çakır, R. Fışkın, and C. Sevgili, "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering & System Safety, vol. 209, p. 107470, 2021.
[26] C. P. Wulandari, C. Ou-Yang, and H.-C. Wang, "Applying mutual information for discretization to support the discovery of rare-unusual association rule in cerebrovascular examination dataset," Expert Systems with Applications, vol. 118, pp. 52-64, 2019.
[27] R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.
[28] W. Altaf, M. Shahbaz, and A. Guergachi, "Applications of association rule mining in health informatics： a survey," Artificial Intelligence Review, vol. 47, pp. 313-340, 2017.
[29] Y. Sato, K. Izui, T. Yamada, and S. Nishiwaki, "Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization," Expert Systems with Applications, vol. 119, pp. 247-261, 2019.
[30] M. Santosh Kumar and K. Balakrishnan, "Development of a model recommender system for agriculture using apriori algorithm," in Cognitive Informatics and Soft Computing： Proceeding of CISC 2017, 2019： Springer, pp. 153-163.
[31] A. Telikani, A. H. Gandomi, and A. Shahbahrami, "A survey of evolutionary computation for association rule mining," Information Sciences, vol. 524, pp. 318-352, 2020.
[32] A. Telikani, A. H. Gandomi, A. Shahbahrami, and M. N. Dehkordi, "Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony," Expert Systems with Applications, vol. 144, p. 113097, 2020.
[33] A. Telikani and A. Shahbahrami, "Data sanitization in association rule mining： An analytical review," Expert Systems with Applications, vol. 96, pp. 406-426, 2018.
[34] 行政院環境保護署，土壤及地下水污染整治法土壤及地下水目，民國99年。
[35] 行政院環境保護署，全國重金屬高污染潛勢農地之管制及調查計畫，民國99年。
[36] 行政院環境保護署，全國重金屬高污染潛勢農地之管制及調查計畫，民國101年。
[37] 行政院環境保護署，全國重金屬高污染潛勢農地之管制及調查計畫，民國105年。
[38] 行政院環境保護署，桃園地區污染農地調查計畫，民國100年。
[39] 桃園市政府環境保護局，桃園縣農地土壤污染控制場址改善計畫監督及驗證工作，民國103年。
[40] 桃園市政府環境保護局，桃園縣農地土壤污染控制場址改善計畫(第二期)監督及驗證工作，民國104年。
[41] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國105年。
[42] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國106年。
[43] 桃園市政府環境保護局，桃園市農地土壤污染控制場址排客土改善計畫監督及驗證工作，民國106年。
[44] 桃園市政府環境保護局，105年-107 年度桃園市農地土壤污染控制場址適當措施改善計畫第3期之3-1)監督及驗證工作-桃園大圳第三、四支線等灌區，民國107年。
[45] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國107年。
[46] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國108年。
[47] 桃園市政府環境保護局，107-109年度桃園市農地土壤污染控制場址適當措施改善計畫(第3期之3-2)監督及驗證工作-桃園大圳第三、四支線等灌區，民國109年。
[48] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國109年。
[49] 桃園市政府環境保護局，108-110年度桃園市農地土壤污染控制場址適當措施改善計畫(第4期)監督及驗證工作，民國110年。
[50] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國110年。
[51] 桃園市政府環境保護局，桃園市土壤及地下水污染調查及查證工作計畫，民國111年。
[52] 土壤及地下水污染整治網，"全國各縣市農地列管及解列情形統計，" 2022. [Online]. Available: https://sgw.epa.gov.tw/SGM/Anonymous/SgmLogin.aspx.
[53] 桃園市政府環境保護局，桃園市農地污染源預防管理計畫，民國112年。
[54] P. Charoen-Ung and P. Mittrapiyanuruk, "Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning," in Recent Advances in Information and Communication Technology 2018: Proceedings of the 14th International Conference on Computing and Information Technology (IC2IT 2018), 2019: Springer, pp. 33-42.

指導教授

陳介豪(Jieh-Haur Chen)

審核日期

2023-7-12

推文