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Machine-learning-based water quality management of river with serial impoundments in the Republic of Korea 1 1 Introduction 2 2 Materials and methods 3 2.1 Study region 3 2.2 Data acquisition, preprocessing, and preparation for ML 5 2.2.1 Data acquisition 5 2.2.2 Data preprocessing 6 2.2.3 Preparation of training, validation, and test datasets for ML implementation 6 3 Machine Learning (ML) algorithms 8 3.1 Random Forest (RF) 8 3.2 Extreme gradient boosting (XGBoost) 8 3.3 Deep Neural Network (DNN) 9 3.4 Long Short-Term Memory (LSTM) 9 3.5 Development of the framework to prioritize total phosphorous management strategies using machine learning algorithms 10 4 Results and discussion 11 4.1 Hyperparameter tuning and performance evaluation of ML models 11 4.2 Comparison of seasonal performance obtained using ML models 13 4.3 Gradient-based analysis of LSTM model to prioritize management strategies 15 4.4 Field practical implications in water management and future perspectives 17 5 Conclusions 18 CRediT authorship contribution statement 18 Declaration of Competing Interest 18 Acknowledgments 19 Appendix A Supporting information 19 Appendix A Statistical parameters of input and output v

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UND

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nonfiction