神经网络在水质预测中的应用

Application of Neural Networks in Water Quality Prediction

摘要: 随着经济的快速发展和人口的不断增长，水资源污染问题日益严峻，水质预测对于环境保护、水资源管理以及人类健康至关重要。神经网络因其强大的非线性映射能力，在水质预测领域得到了广泛应用。本文综述了神经网络在水质预测中的应用，神经网络模型主要包括BP神经网络、RBF神经网络、LSTM、GRU等，同时探讨了数据预处理、输入变量选取等关键环节。神经网络预测水质主要面临模型过拟合、训练数据质量与数量不足、网络结构与参数确定困难等问题。针对这些问题，本文介绍了正则化、早停、数据增强、网格搜索等应对策略。现有研究在模型改进与预测精度提升方面取得了一定成果，但在模型泛化性、实时性等方面仍存在不足。未来，神经网络有望与深度学习、物联网技术融合，并在新领域与新场景得到进一步拓展应用。

Abstract: With rapid economic development and continuous population growth, water pollution has become increasingly severe. Water quality prediction is crucial for environmental protection, water resource management, and human health. Due to their powerful nonlinear mapping capabilities, neural networks have been widely applied in water quality prediction. This paper reviews the applications of neural networks in water quality prediction. The neural network models mainly include BP neural network, RBF neural network, LSTM, GRU, etc. Meanwhile, key aspects such as data preprocessing and input variable selection are discussed. Water quality prediction using neural networks mainly faces challenges such as model overfitting, insufficient quantity and quality of training data, and difficulties in determining network structure and parameters. To address these issues, this paper introduces coping strategies including regularization, early stopping, data augmentation, and grid search. Existing research has achieved certain results in model improvement and prediction accuracy enhancement, but there are still deficiencies in model generalization and real-time performance. In the future, neural networks are expected to integrate with deep learning and IoT technologies, and be further applied in new fields and scenarios.