题目：基于贝叶斯分类的河流水污染源类别贡献判断系统的设计与实现
综合考虑城镇污水处理厂、重点工业污染源等点源排放特征，以及畜禽养殖、农业种植、农村生活等面源排放特征，采用贝叶斯分类方法和模糊理论，分析污染因子同流域内各个行业水污染排放标准、面源产排污特征的相似度，计算各污染源类别对水体污染的贡献指数，判断对水质超标贡献最显著的污染类别，定性解析出影响目标水体的面源污染综合排放特征（关键源类，关键源区、关键源期）和点源污染行业，实现面向常规水质监管的河流水污染的定性源解析。
Beyond Tides and Time: Machine Learning Triumph in Water Quality
Water resources are essential for sustaining human livelihoods and environmental well being. Accurate water quality prediction plays a pivotal role in effective resource management and pollution mitigation. In this study, we assess the effectiveness of five distinct predictive models linear regression, Random Forest, XGBoost, LightGBM, and MLP neural network, in forecasting pH values within the geographical context of Georgia, USA. Notably, LightGBM emerges as the top performing model, achieving the highest average precision. Our analysis underscores the supremacy of tree-based models in addressing regression challenges, while revealing the sensitivity of MLP neural networks to feature scaling. Intriguingly, our findings shed light on a counterintuitive discovery: machine learning models, which do not explicitly account for time dependencies and spatial considerations, outperform spatial temporal models. This unexpected superiority of machine learning models challenges conventional assumptions and highlights their potential for practical applications in water quality prediction. Our research aims to establish a robust predictive pipeline accessible to both data science experts and those without domain specific knowledge. In essence, we present a novel perspective on achieving high prediction accuracy and interpretability in data science methodologies. Through this study, we redefine the boundaries of water quality forecasting, emphasizing the significance of data driven approaches over traditional spatial temporal models. Our findings offer valuable insights into the evolving landscape of water resource management and environmental protection.
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