用混合机器学习模型预测奥迪沙地下水污染风险，精度超98%。
Integrating Boosted learning with Differential Evolution (DE) Optimizer: A Prediction of Groundwater Quality Risk Assessment in Odisha
- 融合梯度提升与差分进化优化，提升预测模型性能。
- 模型在测试集上达0.9809的R²，误差低于0.69。
- 识别钾、氟化物和总硬度为关键污染指标，适合环保决策者使用。
地下水因快速工业化、城市化、过度开采及农业与城市污染源而面临严重威胁。其中镉（Cd）、铬（Cr）、砷（As）、铅（Pb）等重金属在高浓度下可引发神经疾病、肾衰竭及多种癌症。本研究提出一种基于机器学习的预测模型——LCBoost Fusion，用于评估奥迪沙地区地下水质量指数（GWQI）并识别主要污染物。模型经过数据预处理、差分进化（DE）优化超参数调优，并通过交叉验证评估。结果表明，该模型优于单独的CatBoost与LightGBM模型，实现均方根误差（RMSE）0.6829、均方误差（MSE）0.5102、平均绝对误差（MAE）0.3147，以及0.9809的R²得分。特征重要性分析显示，钾（K）、氟化物（F）和总硬度（TH）是影响水质的关键因素。研究证明了机器学习在地下水风险评估中的有效性，所提模型可支持实时监测与风险防控。成果将助力环境机构与政策制定者制定针对性管理策略。未来工作将整合遥感数据，构建交互式决策系统。
原文摘要 · Abstract (English)
Groundwater is eventually undermined by human exercises, such as fast industrialization, urbanization, over-extraction, and contamination from agrarian and urban sources. From among the different contaminants, the presence of heavy metals like cadmium (Cd), chromium (Cr), arsenic (As), and lead (Pb) proves to have serious dangers when present in huge concentrations in groundwater. Long-term usage of these poisonous components may lead to neurological disorders, kidney failure and different sorts of cancer. To address these issues, this study developed a machine learning-based predictive model to evaluate the Groundwater Quality Index (GWQI) and identify the main contaminants which are affecting the water quality. It has been achieved with the help of a hybrid machine learning model i.e. LCBoost Fusion . The model has undergone several processes like data preprocessing, hyperparameter tuning using Differential Evolution (DE) optimization, and evaluation through cross-validation. The LCBoost Fusion model outperforms individual models (CatBoost and LightGBM), by achieving low RMSE (0.6829), MSE (0.5102), MAE (0.3147) and a high R$^2$ score of 0.9809. Feature importance analysis highlights Potassium (K), Fluoride (F) and Total Hardness (TH) as the most influential indicators of groundwater contamination. This research successfully demonstrates the application of machine learning in assessing groundwater quality risks in Odisha. The proposed LCBoost Fusion model offers a reliable and efficient approach for real-time groundwater monitoring and risk mitigation. These findings will help the environmental organizations and the policy makers to map out targeted places for sustainable groundwater management. Future work will focus on using remote sensing data and developing an interactive decision-making system for groundwater quality assessment.
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