Environ Monit Assess. 2014 Feb;186(2):1203-17. doi: 10.1007/s10661-013-3450-6.
The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.
水库水质是水库运行和水质管理的关键因素之一。水体中的溶解氧（DO）是微生物生存的必要条件，也是水生生态系统状态的重要指标。本研究采用反向传播神经网络（BPNN）和自适应神经模糊推理系统（ANFIS）两种人工神经网络（ANN）模型以及多元线性回归（MLR）模型，对台湾北部翡翠水库的 DO 浓度进行估算。神经网络的输入变量确定为水温、pH 值、电导率、浊度、悬浮物、总硬度、总碱度和氨氮。通过比较实测 DO 值和模型模拟值计算平均绝对误差、均方根误差和相关系数，评估 ANN 模型和 MLR 模型的性能。结果表明，ANN 模型的估算性能优于 MLR 模型。通过性能指标比较 BPNN 和 ANFIS 模型，发现 ANFIS 模型在预测 DO 值方面优于 BPNN 模型。研究结果表明，神经网络特别是使用 ANFIS 模型能够以合理的精度预测 DO 浓度，这表明神经网络是台湾水库管理的一种有价值的工具。
