Water Bloom Prediction Research Articles

Monitoring of pollution factors of solidified body leachate in a drilling well site and its influence on the surface water environment

Abstract Aiming at the pollution of the leaching solution of the solidified body in the wellbore, a water eutrophication level evaluation algorithm based on the optimized analytic hierarchy process is proposed from the current situation of many factors affecting the eutrophication of the water body and the difficulty of evaluation. Based on this, the user-oriented PC software monitoring system was developed, which mainly includes three major modules: surface water environment monitoring, water eutrophication evaluation, water bloom prediction and warning, and system assistance. The surface water environment monitoring module can receive and display the surface water environment parameter information in real time, and has the functions of data comparison analysis and historical data search, and the eutrophication assessment and water bloom prediction and warning module can analyze the eutrophication status of water bodies in real time and provide short-term and medium-term warnings for water blooms. This research can promote the control of the leachate pollution of the wellbore in the wellbore and the optimization of the surface water environment.

Prediction and Evaluation of Groundwater Environmental Impact Based on Numerical Simulation Model

Fang, Z.; Gao, H., and Ma, Z., 2020. Prediction and evaluation of groundwater environmental impact based on numerical simulation model. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 286-288. Coconut Creek (Florida), ISSN 0749-0208.The prediction and evaluation of groundwater environment is very important for the utilization and protection of water resources and environment. Therefore, the prediction and evaluation of groundwater environmental impact is studied based on numerical simulation model. The design and implementation of the software part of the float system are explained, mainly around the function of the upper software monitoring system. This system realizes the real-time control of water environment information and the evaluation of water eutrophication in the later stage, as well as the function of water bloom warning and prediction. Then, the optimized hierarchical analysis algorithm is emphasized, which provides an effective decision-making platform for the prevention and control of water bloom. With the addition of the cloud-based network camera, the buoy can monitor the water surface 24 hours in real time, and support video playback, mobile phone watching the water surface image and other functions. The study provides a reference for the evaluation of groundwater environment and the wide application of mathematical models.

Nonlinear dynamic numerical analysis and prediction of complex system based on bivariate cycling time stochastic differential equation

In order to deal with the problem of low precision and ignoring the influence of some factors on the growth system of cyanobacteria, this paper considers the effects of light, water temperature and other influencing factors to analyze cyanobacteria growth system. The time stochastic differential equation of cyanobacteria growth with double nutrient cycling is established. The numerical analysis and the intelligent evolutionary algorithm are used to optimize the steady parameters in the time stochastic differential equation of cyanobacteria growth. The light, water temperature and other factors is used to build time series modeling. The time stochastic parameters and the prediction of cyanobacteria biomass is realized. The bifurcation theory and the central manifold theory were used to analyze the nonlinear dynamics of cyanobacteria growth with double nutrient cycling system, so that the conditions of cyanobacteria bloom and prediction of cyanobacteria blooms are achieved. Compared with the existing research, the method proposed in this paper not only determines the conditions of cyanobacteria bloom, but also improves the accuracy of water bloom prediction, and provides an effective reference for environmental protection departments.

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/(B2+B3) and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.

The Algal Blooms Prediction in the Lakes Based on Remote Sensing Information

The eutrophication phenomenon for lake in our country is obvious. Algal blooms have become one of the major environmental issues in the lake. In view of the traditional single point monitoring and analysis limitations, this paper proposes a bloom prediction model based on remote sensing information. Different light wavelengths has different sensitivity of chlorophyll, so extracting the reflectance values to the chlorophyll-sensitive band from satellite remote sensing information, Building water green index information and corresponding to chlorophyll values of lake-sites monitoring area by latitude and longitude geographic information , thereby build a predictive model of algal bloom based on a remote sensing, realize effective prediction of algae bloom in Taihu lake for coastal ,open ocean water and lake reservoir bloom prediction gives an idea.

An Integrated Evaluative Function and Water Bloom Predicting and Prewarning System

An integrated evaluative function and prediction model and prewarning system for water bloom in lakes based on Elman neural network is proposed in this paper, in which main influence factor of outbreak of water bloom is analyzed by rough set theory. The study of the function involves some aspects: algal average activation energy of photosynthesis, integrated nutritional status index, and transparency, which are considered from the microcosmic level, the macroscopic level and the intuitionistic level respectively. The values of the function are classified properly. Combined with the basic features of outbreak of water-bloom, Elman network is studied from the angles of theory and experiment and a water-bloom prewarning system in short term based on Elman network is established. The results of simulation and application show that: Elman neural network improves the algorithm of BP neural network, it has long-term prediction period, strong generalization ability， high prediction accuracy; and needs a small amount of sample and this model provides an efficient new way for short-term water bloom prediction, And approaching ability of Elman network is more superficial than common static networks and its velocity of convergence is faster.

Intelligent Information System Design on Water Quality Monitoring, Water Bloom Prediction and Emergency Treatment Decision-Making

According to the urgent requirement of water quality monitoring, water bloom prediction and emergency treatment decision-making, a multi-functional information system based on GIS (Geographic Information System) is designed and developed, which integrates water quality monitoring and analysis, water bloom prediction and emergency treatment decision-making. VS2010 platform and SQL Server 2005 database are used in the system, and the remote data communication is realized by GPRS. The system has functions such as lakes and reservoirs’ water quality real-time monitoring, teletransmission and location, water quality data analysis, water bloom prediction and emergency treatment decision-making. The medium-term prediction of water bloom is realized by wavelet neural network, and the optimal choice of water bloom emergency treatment decision-making projects is realized by multi-attribute decision-making theory. This design offers effective decision-making system for environmental protection departments, which also has significant meaning to the protection and renovation of water environment, and its application and promotion have considerable social and economic benefits.

A Catastrophe Model for Water Bloom Prediction: A Case Study of China's Lake Chaohu

ABSTRACT Using a case study of Lake Chaohu, the fifth largest lake in China, we constructed a cusp model for water bloom prediction that used TP (total phosphorus), T (temperature), Chla (chlorophyll-a), and DO (dissolved oxygen). These four parameters were assumed to be the most important factors in eutrophication and water bloom of the lake. The model was found to be accurate, because its relative error was around 10%. What is more convincing, according to the catastrophe discriminant of the cusp model, it could be judged that a discontinuous jump of the aquatic ecosystem occurred in July 2004, in Lake Chaohu. This conclusion is consistent with the fact that water blooms arose in August 2004. The cusp model also showed satisfactory precision when applied to forecast the eutrophication trend and prediction of water bloom in Lake Chaohu in 2005. The case study found that water bloom brought on by eutrophication can be fit and predicted by a catastrophe model. We suggest that catastrophe models would be a constructive approach to forecast and judge the outbreak of water bloom in lakes. In addition, by constructing and studying such catastrophe models, lake managers would be able to simulate the effects of different protection and mitigation projects and enrich the scientific basis for the optimization of these projects as well.