Chlorophyll-a prediction based on the Almon-BP delayed neural network model
Since the impoundment of the Three Gorges Reservoir,the nutrient enrichment status of tributary water bodies in the reservoir area significantly deteriorated,transitioning from riverine to lacustrine ecosystems.The reservoir impoundment resulted in reduced flow velocities of tributary water bodies,leading to increased water transparency and nutrient concentrations conducive to the proliferation of increasing in chlorophyll-a mass concentration.Consequently,typical tributaries such as the Xiangxi River in the Three Gorges Reservoir area experienced frequent outbreaks of increasing in chlorophyll-a mass concentration.These recurrent increase in chlorophyll-a mass concentration events not only degraded water quality and aquatic ecosystems but also posed constraints on the sustainable development of society.Correlation analysis,principal component analysis,and grey relational analysis were utilized to identify and validate significant contributors to increase in chlorophyll-a mass concentration.Subsequently,cross-correlation analysis and the Almon distributed lag model were employed to ascertain factors among the major contributors exhibiting time lags and to determine the optimal lag time.Building upon this analysis,an Almon-BP neural network model was developed to forecast the trends of chlorophyll-a mass concentration.The major contributing factors to increase in chlorophyll-a mass concentration at the Xiangxi River Xiakou included dissolved oxygen,pH,air temperature,solar radiation,wind speed,wind direction,turbidity,rainfall,Three Gorges water level difference,and water temperature.Similarly,significant factors at the Pingyikou of the Xiangxi River included dissolved oxygen,pH,air temperature,solar radiation,wind speed,wind direction,turbidity,rainfall,Three Gorges water level difference,water temperature,redox potential,and Three Gorges water level.Among environmental factors at the Xiakou of the Xiangxi River,air temperature,wind speed,solar radiation,pH,and dissolved oxygen exhibited lag effects on increased in chlorophyll-a mass concentration,with optimal lag times ranging from 2 to 7 days,while other environmental factors did not display time lags.Conversely,at the Pingyikou of the Xiangxi River,factors such as water temperature,air temperature,wind speed,rainfall,solar radiation,turbidity,pH,dissolved oxygen,Three Gorges water level difference,redox potential,and Three Gorges water level exhibited lageffects on chlorophyll-a mass concentration,with optimal lag times ranging from 2 to 10 days.Wind direction did not show lag effects.Comparative analysis of three prediction models the BP neural network model considering all environmental factors,the BP neural network model considering only major contributing factors,and the Almon-BP neural network model considering the optimal lag time of major contributors revealed that the Almon-BP neural network model outperformed the corresponding BP models in predicting chlorophyll-a mass concentration in the Xiangxi River,with lower prediction errors.This underscored the efficacy of the Almon-BP neural network model in enhancing the accuracy of chlorophyll-a mass concentration prediction,which was crucial for early warning and mitigating harmful algal bloom occurrences.
Chlorophyll-a predictionmain contribution factortime-lag effectoptimal lag timeAlmon-BP model
万方数据
维普
