A Fuzzy Logic Model for Early Warning of Algal Blooms in a Tidal-Influenced River

Hanjie Yang,Changjin Zhao,Gang Chen,Fantang Zeng,Zhaoting Chen,Yingxin Ye

doi:10.3390/w13213118

Abstract

Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.

Highlights

As urban, industrial, and agricultural activities rapidly increase, their attendant environmental issues have intensified to the great concern of scientists and the public.Algal blooms are one such issue and have become a serious threat to water resources worldwide [1,2]
2014.Subsequently, Subsequently, construction ofofan this study aimed to develop an early warning index of algae blooms based on a logic fuzzy this study aimed to develop an early warning index of algae blooms based on a fuzzy logic model
The analysis revealed that the dissolved oxygen (DO), NH3 -N, total phosphorous (TP), Vel, and Tem were the most relative variables to agal blooms and should drive the early warning model

Summary

Introduction

Industrial, and agricultural activities rapidly increase, their attendant environmental issues have intensified to the great concern of scientists and the public.Algal blooms are one such issue and have become a serious threat to water resources worldwide [1,2]. Algae blooms induce problems such as depletion of oxygen [3], decreased biodiversity [4,5], and reduced water transparency. These problems pose serious risks to human health [6], fisheries, and [7] water resource sustainability. Of particular importance are early warning techniques aimed at identifying algae blooms before or as they occur [9]. This enables rapid response by the aquaculture industry and other stakeholders at the onset of algae blooms and increases the chance of mitigating their impacts

Objectives

Results

Discussion

Conclusion