scholarly journals A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time

2020 ◽  
Author(s):  
R. Quinn Thomas ◽  
Renato J. Figueiredo ◽  
Vahid Daneshmand ◽  
Bethany J. Bookout ◽  
Laura K. Puckett ◽  
...  
Keyword(s):  
Water Quality ◽  
Data Assimilation ◽  
Water Temperature ◽  
Open Source ◽  
Real Time ◽  
Thermal Conditions ◽  
Temperature Forecasting ◽  
Forecasting System ◽  
Water Quality Forecasting ◽  
Daily Forecast

AbstractFreshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real-time iterative water temperature forecasting system (FLARE – Forecasting Lake And Reservoir Ecosystems). FLARE is composed of: water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475-day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean squared error (RMSE) of daily forecasted water temperatures was 1.13 C at the reservoir’s near-surface (1.0 m) for 7-day ahead forecasts and 1.62C for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87C for 7-day forecasts and 1.20C for 16-day forecasts. FLARE successfully predicted the onset of fall turnover 4-14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near-sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open-source system for lake and reservoir water quality forecasting to improve real-time management.Key PointsWe created a real-time iterative lake water temperature forecasting system that uses sensors, data assimilation, and hydrodynamic modelingOur water quality forecasting system quantifies uncertainty in each daily forecast and is open-source16-day future forecasted temperatures were within 1.4°C of observations over 16 months in a reservoir case study

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

scholarly journals Ensemble data assimilation methods for improving river water quality forecasting accuracy

2020 ◽  
Vol 171 ◽  
pp. 115343 ◽  
Author(s):  
Sibren Loos ◽  
Chang Min Shin ◽  
Julius Sumihar ◽  
Kyunghyun Kim ◽  
Jaegab Cho ◽  
...  
Keyword(s):  
Water Quality ◽  
Data Assimilation ◽  
River Water ◽  
River Water Quality ◽  
Forecasting Accuracy ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Water Quality Forecasting

scholarly journals Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

2020 ◽  
Vol 12 (13) ◽  
pp. 5374 ◽  
Author(s):  
Stephen Stajkowski ◽  
Deepak Kumar ◽  
Pijush Samui ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Temperature ◽  
Real Time ◽  
River Water ◽  
Time Window ◽  
Water Quality Management ◽  
Water Quality Monitoring ◽  
Lstm Network ◽  
River Water Temperature

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

An On-Line Monitoring, Data Management and Forecasting System for the Bedford Ouse River Basin

1984 ◽  
Vol 16 (5-7) ◽  
pp. 295-314 ◽  
Author(s):  
P G Whitehead ◽  
D E Caddy ◽  
R F Templeman
Keyword(s):  
Water Quality ◽  
Data Management ◽  
Real Time ◽  
River Basin ◽  
River System ◽  
Computer Programs ◽  
System A ◽  
On Line ◽  
Forecasting System ◽  
Data Summaries

Data from Automatic water quality monitors and gauging stations on the Bedford Ouse are telemetered to the Cambridge office of the Creat Ouse River Division of the Anglian Water Authority. A mini-computer has been installed to log data from the out-stations, convert data to meaningful units, prepare data summaries, check for alarm conditions and forecast flow and quality up to 80 hours ahead at key locations along the river system. The system provides information on flow and quality conditions in real time and has been used to forecast the movement of pollutants along the river system. A generalised suite of computer programs have been developed for data management and forecasting and a micro-processor controlled water quality monitor is currently under development.

Edge-to-cloud Virtualized Cyberinfrastructure for Near Real-time Water Quality Forecasting in Lakes and Reservoirs

2021 ◽  
Author(s):  
Vahid Daneshmand ◽  
Adrienne Breef-Pilz ◽  
Cayelan C. Carey ◽  
Yuqi Jin ◽  
Yun-Jung Ku ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Lakes And Reservoirs ◽  
Water Quality Forecasting

scholarly journals Development of Black Sea nowcasting and forecasting system

2011 ◽  
Vol 8 (2) ◽  
pp. 917-954 ◽  
Author(s):  
G. K. Korotaev ◽  
T. Oguz ◽  
V. L. Dorofeyev ◽  
S. G. Demyshev ◽  
A. I. Kubryakov ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Black Sea ◽  
Information Exchange ◽  
Global Ocean ◽  
The Black Sea ◽  
General Description ◽  
Forecasting System ◽  
The Eu ◽  
Made In

Abstract. The paper presents the development of the Black Sea community nowcasting and forecasting system under the Black Sea GOOS initiative and the EU framework projects ARENA, ASCABOS and ECOOP. One of the objectives of the Black Sea Global Ocean Observing System project is a promotion of the nowcasting and forecasting system of the Black Sea, in order to implement the operational oceanography in the Black Sea region. The first phase in the realization of this goal was the development of the pilot nowcasting and forecasting system of the Black Sea circulation in the framework of project ARENA funded by the EU. The ARENA project included the implementation of advanced modeling and data assimilation tools for near real time prediction. Further progress in development of the Black Sea nowcasting and forecasting system was made in the frame of ASCABOS project, which was targeted at strengthening the communication system, ensuring flexible and operative infrastructure for data and information exchange between the Black Sea partners and end-users. The improvement of the system was made in the framework of the ECOOP project. As a result it was transformed into a real-time mode operational nowcasting and forecasting system. The paper provides the general description of the main parts of the system: circulation and ecosystem models, data assimilation approaches, the system architecture as well as their qualitative and quantitative calibrations.

scholarly journals Development of Black Sea nowcasting and forecasting system

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 629-649 ◽  
Author(s):  
G. K. Korotaev ◽  
T. Oguz ◽  
V. L. Dorofeyev ◽  
S. G. Demyshev ◽  
A. I. Kubryakov ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Black Sea ◽  
Information Exchange ◽  
Global Ocean ◽  
The Black Sea ◽  
General Description ◽  
Forecasting System ◽  
The Eu ◽  
Made In

Abstract. The paper presents the development of the Black Sea community nowcasting and forecasting system under the Black Sea GOOS initiative and the EU framework projects ARENA, ASCABOS and ECOOP. One of the objectives of the Black Sea Global Ocean Observing System project is a promotion of the nowcasting and forecasting system of the Black Sea, in order to implement the operational oceanography in the Black Sea region. The first phase in the realization of this goal was the development of the pilot nowcasting and forecasting system of the Black Sea circulation in the framework of project ARENA funded by the EU. The ARENA project included the implementation of advanced modeling and data assimilation tools for near real time prediction. Further progress in development of the Black Sea nowcasting and forecasting system was made in the frame of ASCABOS project, which was targeted at strengthening the communication system, ensuring flexible and operative infrastructure for data and information exchange between the Black Sea partners and end-users. The improvement of the system was made in the framework of the ECOOP project. As a result it was transformed into a real-time mode operational nowcasting and forecasting system. The paper provides the general description of the main parts of the system: circulation and ecosystem models, data assimilation approaches, the system architecture as well as their qualitative and quantitative calibrations.

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