College of Science, Guilin University of Technology, Guilin 541004, China; Center for Data analysis and Algorithm Technology, Guilin University of Technology, Guilin 541004, China.
College of Marine Sciences, Beibu Gulf University, Qinzhou 535011, China.
Sci Total Environ. 2020 Apr 20;714:136765. doi: 10.1016/j.scitotenv.2020.136765. Epub 2020 Jan 17.
Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management.
水污染是全环境发展中遇到的具有挑战性的问题。近红外（NIR）光谱是一种用于快速水污染检测的成熟技术。建立和优化校准模型，以寻找具有显著改善预测效果的化学计量学算法。机器学习提高了 NIR 光谱对水污染的准确评估的预测能力。最小二乘支持向量机（LSSVM）算法以数据驱动的方式拟合目标问题的参数。该算法的建模能力主要取决于其核函数。在这项研究中，使用 LSSVM 方法建立了近红外校准模型，用于定量测定化学需氧量，这是水污染水平的关键指标。研究了嵌入在 LSSVM 中的不同核函数的效果。通过使用基于逻辑的神经网络提出了一种新的核函数。与常见的核函数不同，这种新的核函数可以利用深度学习方法进行参数优化。所提出的核函数还增强了模型对过拟合的抵抗力，从而可以合理地利用交叉验证。所提出的新核函数适用于水污染的定量测定，也是水资源管理领域其他问题的有前途的解决方案。
