在沿海海洋沉积物中使用混合机器学习模型与元启发优化算法进行重金属预测
Zaher Mundher Yaseen1, Wan Hanna Melini Wan Mohtar2, Raad Z Homod3
1Civil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia; Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia.
Chemosphere
|January 31, 2024
概括
这项研究引入了先进的模型,用于预测海洋沉积物中的和等重金属. 一个混合模型,RVM-FPA,显著提高了环境管理的预测准确性.
科学领域:
- 环境科学 环境科学
- 计算化学的计算化学
- 海洋地球化学 海洋地球化学
背景情况:
- 人类活动有助于海洋沉积物的重金属 (HM) 污染.
- 对 (As) 和 (Zn) 的准确建模对于环境风险评估至关重要.
- 独立的预测模型通常在捕捉复杂的HM动态方面存在局限性.
研究的目的:
- 开发和评估独立和混合模型,用于预测海洋沉积物中的As和Zn度.
- 评估Elman神经网络 (ENN),增强树算法 (BTA) 和相关性矢量机 (RVM) 的预测性能.
- 为了提高预测准确度,使用混合RVM与鲜花授粉算法 (RVM-FPA) 的RVM.
主要方法:
- 实现独立的ENN,BTA和RVM模型用于As和Zn预测.
- 开发一个混合RVM-FPA模型,整合启发式优化.
- 使用绩效指标 (如PBAIS,MAE),图形方法和累积概率函数 (CDF) 的评估.
- 使用Akaike (AIC) 和Schwarz (SCI) 信息标准与Dickey-Fuller (ADF) 和Philip Perron (PP) 测试进行静止性和可靠性检查.
主要成果:
- RVM-M2和ENN-M2模型分别显示了As和Zn预测的最佳性能.
- 与独立模型相比,混合RVM-FPA模型显示出更高的可靠性和预测准确性.
- 对于As,RVM-FPA的预测准确度提高了5%,对于Zn,预测准确度提高了18%.
结论:
- 智能数据驱动模型和启发式优化对于估计复杂的HM度是有效的.
- RVM-FPA模型提供了一种可靠的方法来预测海洋环境中的重金属.
- 调查结果为环境管理者和利益相关者提供了有价值的见解,帮助他们制定有效的战略.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
55
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
55
Extraction: Advanced Methods
447
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
447


