Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Fisheries, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran.
Environ Monit Assess. 2020 Mar 9;192(4):223. doi: 10.1007/s10661-020-8173-x.
Dissolved oxygen (DO) as one of the most fundamental parameters of water quality plays a vital role in aquatic life. This study was conducted to predict DO, biological oxygen demand (BOD), and chemical oxygen demand (COD) in an intensive rainbow trout rearing system with different biomass (B). The multilayer perceptron (MLP) and the radial basis function (RBF) neural networks were employed for evaluating the impacts of food parameters (crude protein (CP), consumed feed (CF)), fish parameters (different values of B, and weight gain (WG)), and water quality parameters including temperature (T) and flow rate (Q) on variation of DO, BOD, and COD concentrations. This study's results showed that although both MLP and RBF neural networks are capable to estimate DO, BOD, and COD concentrations, RBF neural network showed better performance compared to MLP neural network. The results of sensitivity analysis indicated that the parameter CF has the highest effect on DO concentration estimation. Independent variables CF, CP, WG, and B showed the highest to the lowest rank of impacts on BOD estimation, respectively. The results also illustrated a decreasing trend of the effects on the estimation error of COD changes simulation by all independent variables, including B, T, WG, CF, CP, and Q, respectively. RBF neural network based on better stability and generalization ability with average root mean square error (RMSE) and mean absolute percentage error (MAPE) values of less than 0.12 and 3% was superior to MLP in DO, BOD, and COD concentration prediction. Moreover, CF was identified as the most effective factor in estima12tion process. Based on the present study results, there are direct relationships between DO, BOD, and COD concentrations and water quality parameters, fish parameters, and food parameters. Food parameters relative to fish and water quality parameters imposed the greatest effects. Improvement in feeding process such as application of intelligence feeding methods and change in fish diet and feeding time can considerably reduce losses in production system. Graphical abstract.
溶解氧（DO）是水质的最基本参数之一，对水生生物起着至关重要的作用。本研究旨在预测不同生物量（B）下密集虹鳟养殖系统中的 DO、生化需氧量（BOD）和化学需氧量（COD）。本研究采用多层感知器（MLP）和径向基函数（RBF）神经网络来评估食物参数（粗蛋白（CP）、消耗饲料（CF））、鱼类参数（不同 B 值和增重（WG））以及水质量参数（温度（T）和流速（Q））对 DO、BOD 和 COD 浓度变化的影响。结果表明，虽然 MLP 和 RBF 神经网络都可以用来估算 DO、BOD 和 COD 浓度，但 RBF 神经网络的性能优于 MLP 神经网络。敏感性分析结果表明，参数 CF 对 DO 浓度估算的影响最大。CF、CP、WG 和 B 等独立变量对 BOD 估算的影响程度依次递减。结果还表明，所有独立变量（包括 B、T、WG、CF、CP 和 Q）对 COD 变化模拟的估计误差的影响呈递减趋势。RBF 神经网络具有更好的稳定性和泛化能力，平均均方根误差（RMSE）和平均绝对百分比误差（MAPE）值小于 0.12 和 3%，在 DO、BOD 和 COD 浓度预测方面优于 MLP。此外，CF 被确定为估算过程中的最有效因素。基于本研究结果，DO、BOD 和 COD 浓度与水质参数、鱼类参数和食物参数之间存在直接关系。与鱼类和水质参数相比，食物参数的影响更大。通过应用智能投喂方法和改变鱼的饮食和投喂时间等方式来改善投喂过程，可以显著减少生产系统的损失。图摘要。
