基于LSTM神经网络的流域污染物通量预测

Prediction of watershed pollutant flux based on Long ShortTerm Memory neural network

针对流域水文和污染物迁移转化过程模型受限于模型初始条件、边界条件、数值分辨率、参数敏感等及现有的深度学习模型对污染物通量时间序列数据解析缺少物理机制的问题,提出了基于长短时记忆神经网络(LSTM)的流域污染物通量预测模型。借助深度学习框架Keras,构建了多变量时间序列预测模型。选择气象数据作为流域产汇污过程的驱动因子、前期降雨量作为表征流域土壤干湿程度的指标,基于以上指标在不同降雨强度、月份、水文期的污染物通量的差异性分析,确定了模型的输入端特征;使用基于LSTM的时间模拟器识别了历史数据间的固有特征及输入特征间的复杂非线性关系;通过基于该模型的流域污染物日通量模拟值和实测值的比较,以及与流域分布式水文和污染物迁移转化过程模型(SWAT模型)的对比分析,评价了模型的预测性能,分析了不同输入特征的贡献率,验证了使用该模型预测流域污染物通量的可行性。该预测模型可为流域污染物通量预测提供一种新的思路。

Previous watershed hydrological and pollutant migration and transformation models are greatly affected in simulation effectiveness by their initial conditions,boundary conditions,numerical resolution,and parameter sensitivity;and the existing deep learning models lack consideration of physical mechanisms in analysis of pollutant flux time series data.Aimed at these two problems,this paper presents a watershed pollutant flux prediction model based on a Long Short-Term Memory(LSTM)neural network.First,we construct a multivariable time series prediction model with the aid of the deep learning framework Keras.To determine its input parameters,we use the meteorological data of the watershed as the driving factor of its pollutant production and collection,and select its previous rainfall as the index of its soil dry-wet degree;then,using these indexes,we analyze the differences in pollutant flux under different rainfall intensities in different months and hydrological periods.Hence,the features–meteorology,soil dry-wet degree,and the level,month and hydrological period of the rainfall–can be added to the model input list.A LSTM-based time simulator is used to identify the inherent characteristics of historical data and the complex nonlinear relationship between the input features.The prediction performance of this LSTM pollutant flux model is evaluated by comparing the simulated watershed daily pollutant fluxes with measurements in-situ and using a comparative analysis with the distributed hydrological and pollutant migration and transformation process model(SWAT model)of the watershed.And the contribution rates of different input features are analyzed.Thus,we have verified the feasibility of the LSTM model and demonstrated a new idea for better predictions of watershed pollutant fluxes.