機器學習在水庫入流與濁度預測之應用-以石岡壩為例

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姓名

王彥凱(Yen-Kai Wang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

機器學習在水庫入流與濁度預測之應用-以石岡壩為例
(Application of Machine Learning for Dam Inflow and Turbidity Forecasting – a Case Study of Shih Gang Dam)

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至系統瀏覽論文 (2024-8-10以後開放)

摘要(中)

洪水災害對人類社會、財產、生命影響甚巨，能夠模擬降雨逕流之機器學習是近二十年水文領域重要的研究之一，其中流量與濁度一直是其中的重要指標，兩者對於水資源的利用、水中生態環境以及公共用水都有顯著的影響，因此水庫流量以及水體濁度的預測具有重大意義。每次經歷豪大雨或者颱風的沖刷挾帶大量泥砂流入石岡壩導致濁度過高無法供水，為解決此問題並且掌握其發展過程，一個好的預測模型是必不可少的。
本論文藉由包含以k-近鄰演算法（k- Nearest Neighbors, kNN）進行遺漏值填補；以遞迴神經網路（Recurrent Neural Network, RNN）、長短期記憶（Long Short-Term Memory, LSTM）、Gated Recurrent Unit（GRU）、支援向量回歸（Support Vector Regression, SVR）、隨機森林（Random Forest, RF）等五種模型進行預測，並以隨機搜索（Random Search）、貝葉斯優化（Bayesian Optimization）進行超參數優化，提供一個能夠有效預測未來多個小時的優化策略。
本研究以一階差分方法新增資料特徵提升預測精準度，在預測未來六小時之結果中，並考量實務應用之前提下，壩前流量與濁度預測最佳之模型分別為LSTM與SVR，R-Squared值分別達到0.81與0.77，並有效預測至未來十二小時，R-Squared值分別達到0.56與0.55。在LSTM貝葉斯超參數優化之壩前流量與濁度R-Squared值分別達到0.83與0.68，在濁度預測之表現與標準化資料集、新增差分特徵方法以及隨機搜索之預測結果相比分別從0.31、0.57以及0.57增加至0.68。

摘要(英)

Floods have a significant impact on human society, property, and life. Machine learning to simulate rainfall-runoff has been one of the most critical studies in the field of hydrology in the last two decades, and flow and turbidity have been among the important indicators. A good prediction model is essential to solve this problem and understand the development process.
This paper uses machine learning techniques such as k-Nearest Neighbors (kNN) to filling missing values. The five models include Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR), and Random Forest (RF) for prediction. Random Search and Bayesian Optimization for the hyperparameter optimization. This thesis provides an optimization strategy that can effectively predict the future for many hours.
In this study, the first-order difference method was used to add new data features to improve the accuracy of prediction. The best models for predicting flow and turbidity for the next six hours were LSTM and SVR, with R-Squared values of 0.81 and 0.77, respectively, and for the next 12 hours, with R-Squared values of 0.56 and 0.55, respectively.
In the LSTM hyperparameter optimization results, the Bayesian optimized flow and turbidity R-Squared values reached 0.83 and 0.68, respectively, the performance in turbidity prediction increased from 0.31, 0.57 and 0.57 to 0.68 compared to the prediction results of the standardized dataset, the first-order difference method and the random search, respectively.

關鍵字(中)

★ 機器學習
★ 水文預測
★ 遺漏值填補
★ 超參數優化

關鍵字(英)

★ Machine learning
★ hydrological prediction
★ missing value imputation
★ hyperparameter optimization

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章、緒論 1
1.1 研究背景與動機 1
1.2 問題與目的 1
1.3 研究流程 2
1.4 研究貢獻 4
第二章、文獻探討 5
2.1 機器學習（Machine Learning）相關理論文獻回顧 5
2.1.1 監督式學習- k-近鄰演算法（k- Nearest Neighbors, kNN） 6
2.1.2 監督式學習- 支援向量機 7
2.1.3 監督式學習- 隨機森林 10
2.1.4 監督式學習- 神經網路（Neural Network） 12
2.1.4.1 遞迴神經網路 12
2.1.4.2 長短期記憶 13
2.1.4.3 Gate Recurrent Unit 15
2.1.5 超參數優化 16
2.1.5.1 網格搜索 18
2.1.5.2 隨機搜索 19
2.1.5.3 貝葉斯優化 19
2.2 機器學習在水文預測之應用歷程 22
2.2.1 水位流量預測案例回顧 24
2.2.2 濁度預測案例回顧 33
2.3 綜合評析 38
第三章、研究方法 40
3.1研究架構 40
3.2研究區域 41
3.2.1 石岡壩概況 42
3.2.2 相關水文資料搜集 46
3.3實驗環境 48
3.4 資料前處理 49
3.4.1 遺漏值填補 49
3.4.2 特徵選取-相關係數分析 50
3.4.3 資料集劃分 51
3.4.4 資料標準化 52
3.4.5 資料差分 53
3.5 模型架構 54
3.6 超參數優化 57
3.7 模型評估指標 57
第四章、實驗結果 59
4.1 資料前處理結果 59
4.1.1 遺漏值填補結果 59
4.1.2 特徵選取-相關係數分析結果 61
4.2 模型預測結果 64
4.2.1 RNN 64
4.2.2 LSTM 71
4.2.3 GRU 78
4.2.4 SVR 85
4.2.5 Random Forest 90
4.3 模型比較 95
4.4 超參數優化結果 101
4.4.1 隨機搜索與貝葉斯優化 101
4.4.2 超參數優化比較 108
第五章、結論與建議 109
5.1 結論 109
5.2 建議 111
委員建議與回覆 112
參考文獻 114
附錄A 資料集歷史曲線圖 A-1
附錄B 模型預測結果表 B-1

參考文獻

[1]. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
[2]. Le, X.‐H., Ho, H. V., Lee, G., & Jung, S. (2019). Application of long short‐term memory (LSTM) neural network for flood forecasting. Water, 11(7), 1387.
[3]. Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0–07–042807–7. OCLC 36417892.
[4]. Pearson, K. (1901) On Lines and planes of closest fit to systems of points in space, Philosophical Magz., v.2(6), pp.559-572.
[5]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
[6]. Xiang, Z., Yan, J., & Demir, I. (2020). A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water resources research, 56(1), e2019WR025326.
[7]. 石岡壩（2019）。關於石岡壩。上網日期：2022年2月21日。檢自：https://www.wracb.gov.tw/47808/47809/47810/49514/
[8]. 張炎銘、林廷芳、高穆賓主編. 閱讀水庫行腳台灣：探訪隱身山林的灰色建築. 台北市: 三聯科技教育基金會. (2012). ISBN 978-986-84878-5-7.
[9]. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
[10]. Wang, S. C. (2003). Artificial neural network. In Interdisciplinary computing in java programming (pp. 81-100). Springer, Boston, MA.
[11]. Kalchbrenner, N., & Blunsom, P. (2013, October). Recurrent continuous translation models. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 1700-1709).
[12]. Mikolov, T. (2012). Statistical language models based on neural networks. Presentation at Google, Mountain View, 2nd April, 80(26).
[13]. Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing (pp. 6645-6649). Ieee.
[14]. Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1), 30-42.
[15]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
[16]. Olah, C. Understanding LSTM Networks. Available online: http://colah.github.io/posts/2015-08- Understanding-LSTMs/ (accessed on 23 February 2022).
[17]. Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270.
[18]. Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., ... & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1), 1-37.
[19]. Lin, W. C., & Tsai, C. F. (2020). Missing value imputation: a review and analysis of the literature (2006–2017). Artificial Intelligence Review, 53(2), 1487-1509.
[20]. Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for knn classification. ACM Transactions on Intelligent Systems and Technology (TIST), 8(3), 1-19.
[21]. Zhang, C., Zhu, X., Zhang, J., Qin, Y., & Zhang, S. (2007, May). GBKII: An imputation method for missing values. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 1080-1087). Springer, Berlin, Heidelberg.
[22]. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
[23]. Fu, R., Zhang, Z., & Li, L. (2016, November). Using LSTM and GRU neural network methods for traffic flow prediction. In 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) (pp. 324-328). IEEE.
[24]. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
[25]. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.
[26]. Singh VP (1988) Hydrologic systems: rainfall-runoff modeling, vol 1. Prentice hall, Englewood Cliffs
[27]. Shen, J. (1965). Use of analog models in the analysis of flood runoff (No. 506). US Government Printing Office.
[28]. Hu, C., Wu, Q., Li, H., Jian, S., Li, N., & Lou, Z. (2018). Deep learning with a long short-term memory networks approach for rainfall-runoff simulation. Water, 10(11), 1543.
[29]. Mekanik, F., Imteaz, M. A., Gato-Trinidad, S., & Elmahdi, A. (2013). Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes. Journal of Hydrology, 503, 11-21.
[30]. Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., & Pilla, F. (2017, September). Urban water flow and water level prediction based on deep learning. In Joint European conference on machine learning and knowledge discovery in databases (pp. 317-329). Springer, Cham.
[31]. Pham, B. T., Le, L. M., Le, T. T., Bui, K. T. T., Le, V. M., Ly, H. B., & Prakash, I. (2020). Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845.
[32]. Liu, M., Huang, Y., Li, Z., Tong, B., Liu, Z., Sun, M., ... & Zhang, H. (2020). The applicability of LSTM-KNN model for real-time flood forecasting in different climate zones in China. Water, 12(2), 440.
[33]. Asmel, N. K., Al-Nima, R. R., Mohammed, F. I., Al Saadi, A. M., & Ganiyu, A. A. (2021). Forecasting Effluent Turbidity and pH In Jar Test Using Radial Basis Neural Network. In Towards a Sustainable Water Future: Proceedings of Oman’s International Conference on Water Engineering and Management of Water Resources (pp. 361-370). ICE Publishing.
[34]. Song, C., & Zhang, H. (2020). Study on turbidity prediction method of reservoirs based on long short term memory neural network. Ecological Modelling, 432, 109210.
[35]. Wang, Y., Chen, J., Cai, H., Yu, Q., & Zhou, Z. (2021). Predicting water turbidity in a macro-tidal coastal bay using machine learning approaches. Estuarine, Coastal and Shelf Science, 252, 107276.
[36]. Stevenson, M., & Bravo, C. (2019). Advanced turbidity prediction for operational water supply planning. Decision Support Systems, 119, 72-84.
[37]. Mosavi, A., Ozturk, P., & Chau, K. W. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536.
[38]. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
[39]. Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197-227.
[40]. Frazier, P. I. (2018). A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811.
[41]. Močkus, J. (1975). On Bayesian methods for seeking the extremum. In Optimization techniques IFIP technical conference (pp. 400-404). Springer, Berlin, Heidelberg.
[42]. Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for knn classification. ACM Transactions on Intelligent Systems and Technology (TIST), 8(3), 1-19.
[43]. Lahiri, S. K., & Ghanta, K. C. (2008). The support vector regression with the parameter tuning assisted by a differential evolution technique: Study of the critical velocity of a slurry flow in a pipeline. Chemical Industry and Chemical Engineering Quarterly, 14(3), 191-203.
[44]. Joy, T. T., Rana, S., Gupta, S., & Venkatesh, S. (2019). A flexible transfer learning framework for Bayesian optimization with convergence guarantee. Expert Systems with Applications, 115, 656-672.
[45]. Wikimedia commons user DenisBoigelot. Examples of correlations. In the public domain. (2011). Retireved May 25, 2022, from https://commons.wikimedia.org/wiki/File:Correlation_examples2.svg
[46]. Someka. (2021, November 10). How to Normalize Data in Excel? Retireved May 25, 2022, from https://www.someka.net/blog/how-to-normalize-data-in-excel/
[47]. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
[48]. Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Springer, New York.
[49]. Huang, Q., Mao, J., & Liu, Y. (2012, November). An improved grid search algorithm of SVR parameters optimization. In 2012 IEEE 14th International Conference on Communication Technology (pp. 1022-1026). IEEE.
[50]. Ren-Jun, Z. (1992). The Xinanjiang model applied in China. Journal of hydrology, 135(1-4), 371-381.
[51]. Yao, C., Li, Z. J., Bao, H. J., & Yu, Z. B. (2009). Application of a developed Grid-Xinanjiang model to Chinese watersheds for flood forecasting purpose. Journal of Hydrologic Engineering, 14(9), 923-934.
[52]. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
[53]. Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN′95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
[54]. Hansen, L. D., Stokholm-Bjerregaard, M., & Durdevic, P. (2022). Modeling phosphorous dynamics in a wastewater treatment process using Bayesian optimized LSTM. Computers & Chemical Engineering, 160, 107738.
[55]. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
[56]. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
[57]. Kao, I. F., Zhou, Y., Chang, L. C., & Chang, F. J. (2020). Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting. Journal of Hydrology, 583, 124631.

指導教授

鐘志忠(Chung-Chih Chung)

審核日期

2022-8-11

推文