Machine Learning-Based Prediction of Chlorophyll-a Variations in Receiving Reservoir of World’s Largest Water Transfer Project—A Case Study in the Miyun Reservoir, North China

Qiang Liu,Chunhui Li,Xuan Wang,Nan Zang,Zhenmei Liao

doi:10.3390/w13172406

Qiang Liu, Chunhui Li + Show 3 more

Open Access

https://doi.org/10.3390/w13172406

Copy DOI

Abstract

Although water transfer projects can alleviate the water crisis, they may cause potential risks to water quality safety in receiving areas. The Miyun Reservoir in northern China, one of the receiving reservoirs of the world’s largest water transfer project (South-to-North Water Transfer Project, SNWTP), was selected as a case study. Considering its potential eutrophication trend, two machine learning models, i.e., the support vector machine (SVM) model and the random forest (RF) model, were built to investigate the trophic state by predicting the variations of chlorophyll-a (Chl-a) concentrations, the typical reflection of eutrophication, in the reservoir after the implementation of SNWTP. The results showed that compared with the SVM model, the RF model had higher prediction accuracy and more robust prediction ability with abnormal data, and was thus more suitable for predicting Chl-a concentration variations in the receiving reservoir. Additionally, short-term water transfer would not cause significant variations of Chl-a concentrations. After the project implementation, the impact of transferred water on the water quality of the receiving reservoir would have gradually increased. After a 10-year implementation, transferred water would cause a significant decline in the receiving reservoir’s water quality, and Chl-a concentrations would increase, especially from July to August. This led to a potential risk of trophic state change in the Miyun Reservoir and required further attention from managers. This study can provide prediction techniques and advice on water quality security management associated with eutrophication risks resulting from water transfer projects.

Highlights

Introduction published maps and institutional affilAs a water conservancy project for mitigating water scarcity and improving water quality, water transfer projects are of great significance in alleviating the uneven distribution of water resources to relieve regional water crises and to promote regional socio-economic development and ecological environment improvement [1,2]
After the middle route of the SNWTP was put into operation in December 2014, there was 5.04 × 108 m3 of water transferred into the Miyun Reservoir by 2020
Comparing the machine learning models in our study with Zeng et al.’s mechanical model [49], the results showed that the Mean Absolute Error (MAE) of our model was between 0.0006 to 0.0012 (Table 3), while the MAE of the mechanical model was equal to 0.2177, indicating that our models had better prediction ability than mechanical model for predicting Chl-a concentration variations

Summary

Introduction

As a water conservancy project for mitigating water scarcity and improving water quality, water transfer projects are of great significance in alleviating the uneven distribution of water resources to relieve regional water crises and to promote regional socio-economic development and ecological environment improvement [1,2]. Transferred water can change the hydrologic and hydrodynamic characteristics of receiving reservoirs and disturb the water environment system of receiving reservoirs, which causes variations in water environmental factors and the potential risk of eutrophication [3,4]. As the main source of regional drinking and irrigation water, the water quality of receiving reservoirs is related to regional water security, food safety, human health, and socioeconomic development [5,6]. To ensure water quantity and quality safety for iations.

Objectives

Methods

Results

Discussion

Conclusion