Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Yanhu He,Zhenjie Gong,Yanhui Zheng,Yuanbo Zhang

doi:10.3390/w13202844

Abstract

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

Highlights

Reservoirs play an important role in the supply of inland fresh water resources [1,2].in recent decades, as a result of the growing population and the rapidly developing economy, the problem of reservoir eutrophication has become increasingly serious, resulting in the reduction in biodiversity and degradation of freshwater, and posing a significant threat to water supplies [3,4]
Seven wavebands’ (Band1 Coastal, Band2 Blue, Band3 Green, Band4 Red, Band5 NIR, Band6 SWIR1, and Band7 SWIR2) [33,67] reflectance values of remote sensing images and water quality parameters were selected as the input layer and output layer, respectively, and the number of nodes in the hidden layer varied with each inversion model
A three-layer back propagation (BP) neural network with the structure of 7 − i − 1, namely, seven nodes in the input layer, i nodes in the hidden layer, and one node in the output layer, was constructed

Summary

Introduction

Reservoirs play an important role in the supply of inland fresh water resources [1,2]. In recent decades, as a result of the growing population and the rapidly developing economy, the problem of reservoir eutrophication has become increasingly serious, resulting in the reduction in biodiversity and degradation of freshwater, and posing a significant threat to water supplies [3,4]. It is necessary to strengthen the monitoring and supervision of water quality conditions in reservoirs [5,6]. Traditional water quality monitoring at cross sections is difficult and time and labor consuming, and it is hard to quickly reflect the overall water quality of reservoirs [7]. Remote sensing technology can be used to rapidly and continuously present the water quality condition of the entire water body in a global spatial and multi-temporal manner, with the characteristics of a wide range of monitoring, low cost, and long-term dynamic monitoring [8,9].

Objectives

Methods

Results

Conclusion