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題名 飲用水質即時監控與預測系統的設計與應用
Design and Application of Real-Time Monitoring and Prediction System for Drinking Water Quality
作者 黃吉助
Huang, Authur
貢獻者 蔡子傑
Tsai Tzu-Chieh
黃吉助
Authur Huang
關鍵詞 飲用水
RPI
機器學習
物聯網
Drinking Water
RPI
Machine Learning
IoT
日期 2024
上傳時間 5-八月-2024 13:55:47 (UTC+8)
摘要 如何防範飲用水受到污染一直是全球所努力解決的公共衛生問題。污染的水源會對人類健康產生嚴重影響，引發各種疾病，如胃腸病毒、腹瀉、呼吸系統問題等。雖然目前台灣地區的家用自來水的產生，從原水輸送至淨水場，經過淨水處理程序已安全無虞，但因中間還須經過輸配管線送至用戶住家的過程，以及家戶使用的儲水設施（如儲水塔等），都有機會讓這些處理過的淨水再次受到污染。因此，如何讓民眾可以隨時掌握家中水質狀況，在第一時間獲知飲用水質是否受到污染就至關重要。本研究是開發一個機器學習模型與設計出一個在物聯網平臺上串連數個感測器的一個智慧水質監測與預測系統，除可即時檢測，來瞭解水質的狀況之外，並可進一步的進行水質預測。此系統透過測量水樣的pH值、濁度、總溶解固體（TDS）和溫度，將資訊發送到微控制器Arduino Mega，並將數據上傳，讓使用者可以透過行動裝置或電腦，來讀取即時的監測數據與水質狀況預測。本研究並透過分析自來水、溪水、水塔水、加水站水等水質樣品進行實驗，來驗證這些水樣是否在飲用水的安全數值範圍內。
How to prevent contaminated drinking water has always been a public health problem that the world is trying to solve. Contaminated water sources can have serious effects on human health, causing various diseases such as enterovirus, diarrhea, respiratory problems, etc. Although the current domestic tap water in Taiwan is safe from the raw water to the water purification plant, and the water treatment process is safe, there is a chance that the treated water will be polluted again because it has to be sent to the user's home through the process of transmission and distribution pipelines, as well as the water storage facilities (such as water storage towers) used by the household. Therefore, it is very important for people to know the water quality status of their homes at any time and know whether the drinking water quality is contaminated at the first time. This study aims to develop a machine learning model and design an intelligent water quality monitoring and prediction system with several sensors connected to the Internet of Things (IoT) platform, which can not only detect in real time to understand the status of water quality, but also further predict water quality. The system measures the pH, turbidity, total dissolved solids (TDS) and temperature of the water sample, sends the information to the microcontroller Arduino Mega, and uploads the data to allow users to read real-time monitoring data and water quality predictions from a mobile device or computer. In this study, water quality samples such as tap water, stream water, water tower water and water from water filling stations were analyzed to verify whether these water samples were within the safe range of drinking water.
參考文獻 [1]. 2019 IEEE 4th International Conference on Computer and Communication [2]. M. Mukta, S. Islam, S. das Barman, A. W. Reza and M. S. Hossain Khan, "Iot based Smart Water Quality Monitoring System", pp. 669-673, 2019. [3]. A. N. Prasad, K. A. Mamun, F. R. Islam and H. Haqva, "Smart water quality monitoring system", pp. 1-6, Dec. 2015. [4]. J. Wikstrom, "Evaluating supervised machine learning algorithms to predict recreational fishing success: A multiple species multiple algorithms approach", 2015 [5]. X. Jia, "Detecting Water quality using knn bayesian and decision tree" , pp. 323-327, 2022. [6]. V. Ranković, J. Radulović, I. Radojević, A. Ostojić, and L. Čomić, “Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia,” Ecol. Modell., vol. 221, no. 8, pp. 1239–1244, 2010。 [7]. 王善賢，「台灣地區河川水質狀態指標之建立」，碩士論文，2001。 [8]. 吳冬齡，「歷年河川水質監測數據 之污染程度分析-以中港溪為例」，碩士論文，2002。 [9]. Brown, R.M., N.I. McClelland, R.A. Deininger and R.G. Tozer , “A water quality index – do we dare?” , Water Sewage Wks, 117: 339-343, 1970. [10]. https://www.water.gov.taipei/cp.aspx?n=1068FE6D4EE4A2F1 [11]. A. Abdulkareem, S. Sani, S. Sahran, A. Alyessari, A. Adam, H. Rahman, and B. Abdulkarem, “Predicting covid-19 based on environmental factors with machine learning,” pp. 305–320, 2021, https://doi.org/10.32604/ iasc.2021.015413. [12]. L. Breiman, "Random forrests", Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001. [13]. Springer. James, G., Witten, D., Hastie, T., and Tibshirani, R.”An Introduction to Statistical Learning with Applications” in R. 2nd edition. 2021. [14]. https://www.jianshu.com/p/708dff71df3a. [15]. M. S. U. Chowdury et al., “IoT based real-time river water qual- ity monitoring system,” vol. 155, pp. 161–168, Aug. 2019. [16]. K. Katsanou and H. K. Karapanagioti, "Water Supplies: Water Analysis", pp. 463-469, 2016. [17]. K. A. U. Menon, D. P and M. V Ramesh, "Wireless sensor network for river water quality monitoring in India", pp. 1-7, 2012. [18]. A. T. Demetillo, M. V Japitana and E. B. Taboada, "A system for monitoring water quality in a large aquatic area using wireless sensor network technology", vol. 29, no. 1, pp. 12, 2019. [19]. B. K. Jha, "Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning" , vol. 9, no. 3, 2020. [20]. Z. Kılıç, "The importance of water and conscious use of water", vol. 4, no. 5, pp. 239-241, Oct. 2020. [21]. K. K. Patel, S. M. Patel and P. G. Scholar, "Internet of Things-IOT: Definition Characteristics Architecture Enabling Technologies Application Future Challenges", 2016. [22]. A. Soros, J. E. Amburgey, C. E. Stauber, M. D. Sobsey and L. M. Casanova, "Turbidity reduction in drinking water by coagulation-flocculation with chitosan polymers", vol. 17, no. 2, pp. 204-218, Apr. 2019. [23]. Wikipedia ,https://zh.wikipedia.org/zh-tw/%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97 [24]. 全國法規資料庫, https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=O0040019 [25]. Nattapoj Apichardsilkij,” Basic Comparison Between RandomForest, SVM, and XGBoost”
描述 碩士
國立政治大學
資訊科學系碩士在職專班
110971026
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110971026
資料類型 thesis
dc.contributor.advisor 蔡子傑zh_TW
dc.contributor.advisor Tsai Tzu-Chiehen_US
dc.contributor.author (作者) 黃吉助zh_TW
dc.contributor.author (作者) Authur Huangen_US
dc.creator (作者) 黃吉助zh_TW
dc.creator (作者) Huang, Authuren_US
dc.date (日期) 2024en_US
dc.date.accessioned 5-八月-2024 13:55:47 (UTC+8)-
dc.date.available 5-八月-2024 13:55:47 (UTC+8)-
dc.date.issued (上傳時間) 5-八月-2024 13:55:47 (UTC+8)-
dc.identifier (其他 識別碼) G0110971026en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152767-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 110971026zh_TW
dc.description.abstract (摘要) 如何防範飲用水受到污染一直是全球所努力解決的公共衛生問題。污染的水源會對人類健康產生嚴重影響，引發各種疾病，如胃腸病毒、腹瀉、呼吸系統問題等。雖然目前台灣地區的家用自來水的產生，從原水輸送至淨水場，經過淨水處理程序已安全無虞，但因中間還須經過輸配管線送至用戶住家的過程，以及家戶使用的儲水設施（如儲水塔等），都有機會讓這些處理過的淨水再次受到污染。因此，如何讓民眾可以隨時掌握家中水質狀況，在第一時間獲知飲用水質是否受到污染就至關重要。本研究是開發一個機器學習模型與設計出一個在物聯網平臺上串連數個感測器的一個智慧水質監測與預測系統，除可即時檢測，來瞭解水質的狀況之外，並可進一步的進行水質預測。此系統透過測量水樣的pH值、濁度、總溶解固體（TDS）和溫度，將資訊發送到微控制器Arduino Mega，並將數據上傳，讓使用者可以透過行動裝置或電腦，來讀取即時的監測數據與水質狀況預測。本研究並透過分析自來水、溪水、水塔水、加水站水等水質樣品進行實驗，來驗證這些水樣是否在飲用水的安全數值範圍內。zh_TW
dc.description.abstract (摘要) How to prevent contaminated drinking water has always been a public health problem that the world is trying to solve. Contaminated water sources can have serious effects on human health, causing various diseases such as enterovirus, diarrhea, respiratory problems, etc. Although the current domestic tap water in Taiwan is safe from the raw water to the water purification plant, and the water treatment process is safe, there is a chance that the treated water will be polluted again because it has to be sent to the user's home through the process of transmission and distribution pipelines, as well as the water storage facilities (such as water storage towers) used by the household. Therefore, it is very important for people to know the water quality status of their homes at any time and know whether the drinking water quality is contaminated at the first time. This study aims to develop a machine learning model and design an intelligent water quality monitoring and prediction system with several sensors connected to the Internet of Things (IoT) platform, which can not only detect in real time to understand the status of water quality, but also further predict water quality. The system measures the pH, turbidity, total dissolved solids (TDS) and temperature of the water sample, sends the information to the microcontroller Arduino Mega, and uploads the data to allow users to read real-time monitoring data and water quality predictions from a mobile device or computer. In this study, water quality samples such as tap water, stream water, water tower water and water from water filling stations were analyzed to verify whether these water samples were within the safe range of drinking water.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 論文介紹 1 第二節 研究動機 2 第三節 文獻探討 4 第四節 論文架構 5 第二章 方法論 6 第一節 理論基礎_水質指標 6 第二節 機器學習 10 一、 演算法選擇 10 二、 訓練資料準備 14 三、 模型訓練結果 18 第三節 系統介紹 19 一、 系統架構 19 二、 系統流程 21 三、 系統軟體 22 四、 檢驗設備 24 第四節 整體檢測設備效能說明 26 第三章 試驗驗證 27 第一節 檢測試驗 27 第二節 自動監測 29 第四章 結果與討論 30 第一節 檢測試驗 30 第二節 自動監測 36 第五章 結論 39 參考文獻 41zh_TW
dc.format.extent 4362577 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110971026en_US
dc.subject (關鍵詞) 飲用水zh_TW
dc.subject (關鍵詞) RPIzh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 物聯網zh_TW
dc.subject (關鍵詞) Drinking Wateren_US
dc.subject (關鍵詞) RPIen_US
dc.subject (關鍵詞) Machine Learningen_US
dc.subject (關鍵詞) IoTen_US
dc.title (題名) 飲用水質即時監控與預測系統的設計與應用zh_TW
dc.title (題名) Design and Application of Real-Time Monitoring and Prediction System for Drinking Water Qualityen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1]. 2019 IEEE 4th International Conference on Computer and Communication [2]. M. Mukta, S. Islam, S. das Barman, A. W. Reza and M. S. Hossain Khan, "Iot based Smart Water Quality Monitoring System", pp. 669-673, 2019. [3]. A. N. Prasad, K. A. Mamun, F. R. Islam and H. Haqva, "Smart water quality monitoring system", pp. 1-6, Dec. 2015. [4]. J. Wikstrom, "Evaluating supervised machine learning algorithms to predict recreational fishing success: A multiple species multiple algorithms approach", 2015 [5]. X. Jia, "Detecting Water quality using knn bayesian and decision tree" , pp. 323-327, 2022. [6]. V. Ranković, J. Radulović, I. Radojević, A. Ostojić, and L. Čomić, “Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia,” Ecol. Modell., vol. 221, no. 8, pp. 1239–1244, 2010。 [7]. 王善賢，「台灣地區河川水質狀態指標之建立」，碩士論文，2001。 [8]. 吳冬齡，「歷年河川水質監測數據 之污染程度分析-以中港溪為例」，碩士論文，2002。 [9]. Brown, R.M., N.I. McClelland, R.A. Deininger and R.G. Tozer , “A water quality index – do we dare?” , Water Sewage Wks, 117: 339-343, 1970. [10]. https://www.water.gov.taipei/cp.aspx?n=1068FE6D4EE4A2F1 [11]. A. Abdulkareem, S. Sani, S. Sahran, A. Alyessari, A. Adam, H. Rahman, and B. Abdulkarem, “Predicting covid-19 based on environmental factors with machine learning,” pp. 305–320, 2021, https://doi.org/10.32604/ iasc.2021.015413. [12]. L. Breiman, "Random forrests", Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001. [13]. Springer. James, G., Witten, D., Hastie, T., and Tibshirani, R.”An Introduction to Statistical Learning with Applications” in R. 2nd edition. 2021. [14]. https://www.jianshu.com/p/708dff71df3a. [15]. M. S. U. Chowdury et al., “IoT based real-time river water qual- ity monitoring system,” vol. 155, pp. 161–168, Aug. 2019. [16]. K. Katsanou and H. K. Karapanagioti, "Water Supplies: Water Analysis", pp. 463-469, 2016. [17]. K. A. U. Menon, D. P and M. V Ramesh, "Wireless sensor network for river water quality monitoring in India", pp. 1-7, 2012. [18]. A. T. Demetillo, M. V Japitana and E. B. Taboada, "A system for monitoring water quality in a large aquatic area using wireless sensor network technology", vol. 29, no. 1, pp. 12, 2019. [19]. B. K. Jha, "Cloud-Based Smart Water Quality Monitoring System using IoT Sensors and Machine Learning" , vol. 9, no. 3, 2020. [20]. Z. Kılıç, "The importance of water and conscious use of water", vol. 4, no. 5, pp. 239-241, Oct. 2020. [21]. K. K. Patel, S. M. Patel and P. G. Scholar, "Internet of Things-IOT: Definition Characteristics Architecture Enabling Technologies Application Future Challenges", 2016. [22]. A. Soros, J. E. Amburgey, C. E. Stauber, M. D. Sobsey and L. M. Casanova, "Turbidity reduction in drinking water by coagulation-flocculation with chitosan polymers", vol. 17, no. 2, pp. 204-218, Apr. 2019. [23]. Wikipedia ,https://zh.wikipedia.org/zh-tw/%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97 [24]. 全國法規資料庫, https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=O0040019 [25]. Nattapoj Apichardsilkij,” Basic Comparison Between RandomForest, SVM, and XGBoost”zh_TW