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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 駱尚廉(Shang-Lien Lo) | |
| dc.contributor.author | Yi-Ding Hong | en |
| dc.contributor.author | 洪一丁 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:26:32Z | - |
| dc.date.available | 2021-07-20 | |
| dc.date.available | 2022-11-23T09:26:32Z | - |
| dc.date.copyright | 2021-07-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-08 | |
| dc.identifier.citation | Bagherzadeh, F., Mehrani, M.-J., Basirifard, M., and Roostaei, J. (2021). Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance. Journal of Water Process Engineering, 41, 102033. doi: https://doi.org/10.1016/j.jwpe.2021.102033 Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984). Classification and regression trees: CRC press. Chen, T., and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Paper presented at the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. Del Moro, G., Barca, E., De Sanctis, M., Mascolo, G., and Di Iaconi, C. (2016). Gross parameters prediction of a granular-attached biomass reactor by means of multi-objective genetic-designed artificial neural networks: touristic pressure management case. Environmental Science and Pollution Research, 23(6), 5549-5565. Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., and Zeng, W. (2019). Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agricultural Water Management, 225, 105758. doi: https://doi.org/10.1016/j.agwat.2019.105758 Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems: O'Reilly Media. Guo, H., Jeong, K., Lim, J., Jo, J., Kim, Y. M., Park, J.-p., Kim, J. H., and Cho, K. H. (2015). Prediction of effluent concentration in a wastewater treatment plant using machine learning models. Journal of Environmental Sciences, 32, 90-101. Harrison, J. W., Lucius, M. A., Farrell, J. L., Eichler, L. W., and Relyea, R. A. (2021). Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of The Total Environment, 763, 143005. doi: https://doi.org/10.1016/j.scitotenv.2020.143005 Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30, 3146-3154. Liu, J., Wu, Y., Wu, C., Muylaert, K., Vyverman, W., Yu, H.-Q., Muñoz, R., and Rittmann, B. (2017). Advanced nutrient removal from surface water by a consortium of attached microalgae and bacteria: a review. Bioresource technology, 241, 1127-1137. Lu, H., and Ma, X. (2020). Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere, 249, 126169. doi: https://doi.org/10.1016/j.chemosphere.2020.126169 Müller, A. C., and Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists: ' O'Reilly Media, Inc.'. Metcalf, L., Eddy, H. P., and Tchobanoglous, G. (1991). Wastewater engineering: treatment, disposal, and reuse (Vol. 4): McGraw-Hill New York. Mohammad, A. T., Al-Obaidi, M. A., Hameed, E. M., Basheer, B. N., and Mujtaba, I. M. (2020). Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm. Journal of Water Process Engineering, 33, 100993. Rodríguez, D. C., Ramírez, O., and Mesa, G. P. (2011). Behavior of nitrifying and denitrifying bacteria in a sequencing batch reactor for the removal of ammoniacal nitrogen and organic matter. Desalination, 273(2-3), 447-452. Salgot, M., and Folch, M. (2018). Wastewater treatment and water reuse. Current Opinion in Environmental Science Health, 2, 64-74. Shi, S., and Xu, G. (2019). Identification of phosphorus fractions of biofilm sludge and phosphorus release, transformation and modeling in biofilm sludge treatment related to pH. Chemical Engineering Journal, 369, 694-704. doi: https://doi.org/10.1016/j.cej.2019.03.120 Su, Y., and Zhao, Y. (2020). Prediction of Downstream BOD based on Light Gradient Boosting Machine Method. Paper presented at the 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). WHO, G. (2011). Guidelines for drinking-water quality. World Health Organization, 216, 303-304. Wiesmann, U., Choi, I. S., and Dombrowski, E.-M. (2007). Fundamentals of biological wastewater treatment: John Wiley Sons. Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., and Wang, J. (2020). Tackling environmental challenges in pollution controls using artificial intelligence: a review. Science of The Total Environment, 699, 134279. Yu, P., Gao, R., Zhang, D., and Liu, Z.-P. (2021). Predicting coastal algal blooms with environmental factors by machine learning methods. Ecological Indicators, 123, 107334. doi: https://doi.org/10.1016/j.ecolind.2020.107334 Yu, Z., Yousaf, K., Ahmad, M., Yousaf, M., Gao, Q., and Chen, K. (2020). Efficient pyrolysis of ginkgo biloba leaf residue and pharmaceutical sludge (mixture) with high production of clean energy: Process optimization by particle swarm optimization and gradient boosting decision tree algorithm. Bioresource technology, 304, 123020. doi: https://doi.org/10.1016/j.biortech.2020.123020 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80108 | - |
| dc.description.abstract | 廢污水中的氨氮是水體常見污染物，也是現今污水廠需要去除的主要物質之一。透過預測放流水氨氮，可用於輔助工作人員之最佳化操作，降低污水廠運作成本。因此，本研究將使用機械學習模型對放流水水中的氨氮進行預測，計算預測結果與實際量測值差異，選擇最適合的模型。 本研究使用迪化污水處理廠 2020 年一月至十月之每小時水質數據，使用氫離子濃度指數、水溫、導電度、化學需氧量、氨氮及懸浮固體為原始資料，經由特徵值篩選後，透過 XGBoost、梯度提升機模型、LightGBM、隨機森林模型、極度隨機樹五種機械學習模型，分別對十一月第一週放流水之氨氮進行預測。得到訓練結果後進行參數調整以優化模型，最後將訓練集和驗證集數據整合，得到最終模型。結果顯示，五種模型之準確率分別為 84.8%、40.8%、70.8%、85% 和 40%，其中 XGBoost 和隨機森林模型具有較好的預測準確率，梯度提升機和極度隨機樹模型的評鑒指標與前二者差距不大，但預測結果並不理想，推測與輸入數據的雜訊有關。此外，峰值的預測上，XGBoost 模型有更好的效果。研究表明，使用 XGBoost 等機器學習模型可在一定程度上預測放流水之氨氮濃度。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:26:32Z (GMT). No. of bitstreams: 1 U0001-0707202118120400.pdf: 4602693 bytes, checksum: b37912d471ab9b183ab8551b1752f05b (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 目錄 第一章 前言 1 1.1 研究緣起 1 1.2 研究目的 2 第二章 文獻回顧 3 2.1 XGBoost 3 2.2 梯度提升機（Gradient boosting machine） 7 2.3 LigthGBM 9 2.4 隨機森林 極端隨機樹 (Random Forest Extra tree) 12 第三章 研究方法與過程 15 3.1 研究流程 15 3.2 XGBoost (Extreme Gradient Boosting) 17 3.3 梯度提升機 (Gradient boosting machine) 20 3.4 LigthGBM (Light Gradient Boosting Machine) 23 3.4.1 單邊梯度採樣 (Gradient-based One-Side Sampling，GOSS) 23 3.4.2 互斥特徵綁定 (Exclusive Feature Bundling，EFB) 25 3.4.3 決策樹生長策略 27 3.5 隨機森林 極端隨機樹 (Random Forest Extra tree) 28 3.5.1 隨機森林（Random Forest） 28 3.5.2 極端隨機樹（Extra tree） 29 3.6 模式評鑒指標 30 第四章 結果與討論 32 4.1 模式率定資料概述 32 4.2 模型特徵值選擇 36 4.3 機械學習模型選擇 38 4.4 XGBoost預測結果 39 4.5 GBM預測結果 43 4.6 LightGBM預測結果 47 4.7 RF ET預測結果 51 4.8 模型對比 59 第五章 結論與建議 61 5.1 結論 61 5.2 建議 62 參考文獻 63 | |
| dc.language.iso | zh-TW | |
| dc.subject | 隨機森林模型 | zh_TW |
| dc.subject | 水質預測 | zh_TW |
| dc.subject | XGBoost | zh_TW |
| dc.subject | LightGBM | zh_TW |
| dc.subject | 氨氮 | zh_TW |
| dc.subject | Random Forest model | en |
| dc.subject | XGBoost | en |
| dc.subject | Ammonia | en |
| dc.subject | LightGBM | en |
| dc.subject | Water quality prediction | en |
| dc.title | 應用機械學習預測污水廠放流水之氨氮 | zh_TW |
| dc.title | Prediction of Ammonia in Effluent of Wastewater Treatment Plant by Mechanical Learning | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 闕蓓德(Hsin-Tsai Liu),張嘉玲(Chih-Yang Tseng) | |
| dc.subject.keyword | 氨氮,水質預測,XGBoost,LightGBM,隨機森林模型, | zh_TW |
| dc.subject.keyword | Ammonia,Water quality prediction,XGBoost,LightGBM,Random Forest model, | en |
| dc.relation.page | 65 | |
| dc.identifier.doi | 10.6342/NTU202101330 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-07-08 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 環境工程學研究所 | zh_TW |
| 顯示於系所單位： | 環境工程學研究所 | |
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