通过将增强学习与DE优化器集成,预测地下水质量评估
Sonalika Subudhi1, Alok Kumar Pati2, Sephali Bose1
1Department of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
Scientific reports
|December 1, 2025
概括
一个新的混合机器学习模型,LCBoost融合,准确预测地下水质量,并识别关键污染物,如和化物. 该工具有助于可持续的地下水管理和污染地区的风险减轻.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文地质学 水文地质学
背景情况:
- 工业化和采矿等人类活动严重降低了地下水质量.
- 过多的矿物质 (,,,,) 对健康构成重大风险.
- 印度的苏金达山谷地区面临着严重的地下水污染挑战.
研究的目的:
- 开发一个用于评估地下水质量指数 (GWQI) 的预测机器学习模型.
- 为了确定影响地下水安全的关键污染物在奥迪沙州贾普尔区.
- 为了引入和验证一种新的混合机器学习模型,LCBoost fusion.
主要方法:
- 收集和分析了1989年的地下水样本,以检测物理化学参数.
- 开发了一个混合LCBoost融合模型,结合了CatBoost和LightGBM算法.
- 进行了特征重要性分析,以确定主要的地下水污染物.
主要成果:
- 该LCBoost融合模型实现了高精度 (R2=0.9810) 的低误差指标 (RMSE=0.6826,MSE=0.5100,MAE=0.3148).
- (K), (F) 和总硬度 (TH) 被确定为地下水污染的最重要的指标.
- 混合动力车型的表现优于单个的CatBoost和LightGBM车型.
结论:
- 机器学习,特别是LCBoost Fusion模型,为评估地下水质量风险提供了可靠的方法.
- 这些发现支持实时地下水监测和风险减轻策略.
- 这项研究为决策者和环境组织提供了关于可持续地下水管理的宝贵见解.
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