论文标题
使用复发神经网络的多元水质参数预测模型
A multivariate water quality parameter prediction model using recurrent neural network
论文作者
论文摘要
全球水资源的退化是一个非常关心的问题，尤其是对于人类的生存。对现有水资源的有效监控和管理对于实现和维持最佳水质是必要的。水资源质量的预测将有助于及时确定可能的问题领域，从而提高水管理效率。这项研究的目的是通过应用专门的复发神经网络（RNN），长期记忆（LSTM）和使用历史水质数据来开发基于水质参数的水质预测模型。开发了多变量单个和多个步骤LSTM模型，使用整流的线性单元（relu）激活函数和均方根均方根传播（RMSPROP）优化器。单步模型达到了0.01 mg/L的误差，而多步模型的均方根平方误差（RMSE）为0.227 mg/l。
The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.
