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评估物理信息的神经网络,用于预测水污染风险和环境可持续性

Saima Rashid¹, Husnain Abbas², Ilyas Ali³

¹Department of Mathematics, Government College University, 38000, Faisalabad, Pakistan. saimarashid@gcuf.edu.pk.

Scientific reports

|December 8, 2025

概括

一个新的物理信息神经网络 (PINN) 准确地预测尼罗河水质,整合pH,TDS,EC和水平. 这种先进的模型通过识别污染驱动因素和改善工业排放,盐度和灌的预测,有助于可持续管理.

科学领域:

环境科学与工程环境科学与工程
水资源管理水资源管理
环境监测中的人工智能

背景情况:

尼罗河等主要流域日益增长的污染需要先进的水质监测.
现有的模型往往忽略了水质参数 (pH,TDS,EC,Na) 之间的相互依赖性,从而限制了准确性.
传统的机器学习方法缺乏可持续水资源管理的适应性和物理解释性.

研究的目的:

开发一个新的物理信息神经网络 (PINN) 框架,用于共同预测关键水质指标.
整合水力动力学和化学知识,以改善输入分类和特征相关性.
评估模型在工业排放监管,盐度管理和灌规划场景下的性能.

主要方法:

开发一个PINN框架,与优化提升技术集成.
结合了先前的水力动力学和化学知识,自适应加权和深度交互模块.
利用物理受约束的损失函数来确保生态过程的一致性.

主要成果:

与传统方法相比,PINN表现出优越的性能,R2值达到0.945-0.999和低RMSE (0.012-0.088).
现场分析表明,TDS,EC和Na值经常超过,特别是在干旱季节,而pH值保持在标准范围内.
解释性分析确定了灌强度,盐度负载和工业废水作为水质动态的主要驱动因素.

结论:

拟议的PINN框架显著提高了水质监测的预测准确性和计算效率.
该模型为监管决策,盐度控制优化和可持续灌规划提供了一个实用的工具.
这种方法为减轻污染风险和确保尼罗河的长期生态可持续性提供了可行的解决方案.

关键词:

联合估计联合估计平均绝对误差的平均值.平均二次错误的平均值.神经网络优化神经网络优化非线性PDES的使用方法基于物理学的神经网络.污染水的风险污染水的风险

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