Abstract The paper investigates water quality prediction using machine learning, with a focus on the Gated Recurrent Unit (GRU) model. As water pollution intensifies due to industrialization and urbanization, accurate forecasting becomes critical for environmental protection and sustainable water resource management. The study reviews existing approaches, including mechanistic models like Streeter Phelps and WASP, and data driven non mechanistic models such as ARIMA, SVR, and... The paper investigates water quality prediction using machine learning, with a focus on the Gated Recurrent Unit (GRU) model. As water pollution intensifies due to industrialization and urbanization, accurate forecasting becomes critical for environmental protection and sustainable water resource management. The study reviews existing approaches, including mechanistic models like Streeter Phelps and WASP, and data driven non mechanistic models such as ARIMA, SVR, and deep learning methods. Among deep learning architectures, GRU a simplified variant of LSTM offers strong performance in capturing long term temporal dependencies in time series data while maintaining lower computational complexity. The authors construct a GRU based water quality prediction model using monthly pH and dissolved oxygen (DO) data from China's Surface Water Quality Automatic Monitoring system (2011-2018). After preprocessing and normalization, the GRU model is trained on data from 2011-2017 and tested on 2018 data. Results show that GRU outperforms traditional RNNs and LSTMs in prediction accuracy for both pH and DO levels. The study concludes that GRU provides an efficient and precise solution for water quality forecasting, thereby supporting better decisionmaking in environmental management. Future work may focus on optimizing model architecture and feature selection to enhance robustness and generalization further. This research highlights the potential of deep learning, particularly GRU networks, in tackling real world environmental challenges. Read More Read Less

Abstract The paper explores the application of data mining techniques specifically the LDA (Latent Dirichlet Allocation) topic model and the Apriori association rule algorithm to enhance personalized tourism route recommendations. As tourism shifts from standardized group itineraries toward individualized experiences, the study addresses inefficiencies in current travel planning methods, which often over look user preferences. By analyzing user generated content such as reviews, browsing histories,... The paper explores the application of data mining techniques specifically the LDA (Latent Dirichlet Allocation) topic model and the Apriori association rule algorithm to enhance personalized tourism route recommendations. As tourism shifts from standardized group itineraries toward individualized experiences, the study addresses inefficiencies in current travel planning methods, which often over look user preferences. By analyzing user generated content such as reviews, browsing histories, and click behaviors, the LDA model uncovers latent thematic interests and sentiment trends within tourism related text data. Meanwhile, the Apriori algorithm identifies frequent associations among tourist attractions and services, enabling the construction of optimized, preference aligned itineraries. The proposed recommendation system features a three tier architecture (application, logic, and data processing layers) that integrates real time user data to refine suggestions dynamically. The research demonstrates that combining LDA for topic and sentiment analysis with Apriori for association mining improves the accuracy, relevance, and personalization of travel recommendations. This approach not only enhances user satisfaction but also boosts competitiveness for tourism enterprises by aligning offerings with actual traveler needs. The study concludes that these datadriven methods effectively address information overload and preference ambiguity in modern tourism, marking a significant step toward intelligent, personalized travel planning in the big data era. Read More Read Less

Abstract The paper explores the development of an English teaching resource library enhanced by a personalized information recommendation model based on the collaborative filtering algorithm. It highlights current challenges in China's higher education context, such as low adoption rates of digital English resources, insufficient standardization, poor inter institutional communication, and static, disorganized content that fails to meet learner's diverse needs. The author argues that integrating... The paper explores the development of an English teaching resource library enhanced by a personalized information recommendation model based on the collaborative filtering algorithm. It highlights current challenges in China's higher education context, such as low adoption rates of digital English resources, insufficient standardization, poor inter institutional communication, and static, disorganized content that fails to meet learner's diverse needs. The author argues that integrating big data and intelligent algorithms can transform traditional, static resource libraries into dynamic, interactive platforms. Collaborative filtering is proposed as a solution to overcome issues like data sparsity and imprecise recommendations by analyzing user behavior such as click frequency and browsing history to predict preferences and deliver tailored English learning materials. The paper outlines a two layer recommendation system structure: a user interaction layer and a recommendation response layer. Experimental results from a sample of 2,000 university students show that those using the recommendation enhanced resource library demonstrated measurable improvements in English proficiency, whereas the control group did not. The study concludes that the collaborative filtering algorithm significantly boosts recommendation accuracy and learning outcomes, supporting more efficient, personalized, and engaging English education in digital environments. This approach not only optimizes resource utilization but also aligns teaching content with individual learner needs, marking a meaningful advancement in intelligent educational platforms. Read More Read Less