Application of message-passing interface technology to water quality forecasting model of Jiaozhou Bay

2011 ◽  
Vol 8 (4) ◽  
pp. 395-398
Author(s):  
Weijian Huang ◽  
Pei Niu ◽  
Wei Du
Keyword(s):  
Water Quality ◽  
Message Passing ◽  
Message Passing Interface ◽  
Jiaozhou Bay ◽  
Forecasting Model ◽  
Water Quality Forecasting

scholarly journals Artificial Neural Network Based Water Quality Forecasting Model for Ganga River

2019 ◽  
Vol 8 (6) ◽  
pp. 2778-2785
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Research Work ◽  
River Pollution ◽  
Ganga River ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Water Quality Forecasting ◽  
Artificial Neural

Development of river water quality forecasting model (RWQFM) created using the concept of artificial neural network (ANN) for the river Ganga, India still has not been done as far as best awareness of the authors. In this research work an effort have been made for developing such model first time for the stream Ganga in the stretch from Devprayag to Roorkee, Uttarakhand, India by choosing five testing stations along this waterway. The month to month exploratory dataset for the time arrangement of 2001 to 2015 including four water quality parameters was taken. Using one of the proficient machine learning approach called ANN an optimal model is developed by conducting several experiments in Weka data mining tool. In advance the water quality is forecasted for next 12 months and the forecasting accuracy is determined using various performance measures. The computation of 12-steps ahead WQ indicated that the water comes out to be suitable for drinking throughout the year 2016 only at three stations: Devprayag, Rishikesh and Roorkee. At Haridwar station, the water is also comes out to be of best quality but only in nine months. In last quarter of 2016, a little degradation at Haridwar station while a crucial deterioration was noticed at Jwalapur site. The results showed that the proposed WQ model is more efficient in terms of the forecasting accuracy. At Rishikesh station the developed forecasting model achieved a noteworthy accuracy of 100%. Thus, the proposed ANN forecasting model is verified as an effective model and concluded that in overall the WQ of the Ganga River in this stretch is fine in 2016. Also, ANN has proven its significance as an efficient tool in the forecasting domain. Such models will definitely be helpful for the water management bodies in order to control the river pollution and consequently help the society as well

scholarly journals Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2661
Author(s):  
Nigel W. T. Quinn ◽  
Michael K. Tansey ◽  
James Lu
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Forecasting Model ◽  
Assimilative Capacity ◽  
Web Based ◽  
Salt Load ◽  
San Joaquin River ◽  
Beneficial Uses ◽  
Water Quality Forecasting ◽  
Salinity Management

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

Assessement of Water Quality Parameters in Some Potential Pollutant Areas from Romania

2018 ◽  
Vol 69 (8) ◽  
pp. 2045-2049
Author(s):  
Catalina Gabriela Gheorghe ◽  
Andreea Bondarev ◽  
Ion Onutu
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Chemical Parameters ◽  
Water Quality Data ◽  
Pollutant Sources ◽  
Water Quality Forecasting

Monitoring of environmental factors allows the achievement of some important objectives regarding water quality, forecasting, warning and intervention. The aim of this paper is to investigate water quality parameters in some potential pollutant sources from northern, southern and east-southern areas of Romania. Surface water quality data for some selected chemical parameters were collected and analyzed at different points from March to May 2017.

Multi-level Parallelization of Genotype Imputation on Supercomputers

2020 ◽  
Vol 15 ◽  
Author(s):  
Weiwen Zhang ◽  
Long Wang ◽  
Theint Theint Aye ◽  
Juniarto Samsudin ◽  
Yongqing Zhu
Keyword(s):  
Association Study ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Genome Wide Association Study ◽  
Job Scheduling ◽  
Genotype Imputation ◽  
Job Level ◽  
Multi Level ◽  
High Performance Requirement

Background: Genotype imputation as a service is developed to enable researchers to estimate genotypes on haplotyped data without performing whole genome sequencing. However, genotype imputation is computation intensive and thus it remains a challenge to satisfy the high performance requirement of genome wide association study (GWAS). Objective: In this paper, we propose a high performance computing solution for genotype imputation on supercomputers to enhance its execution performance. Method: We design and implement a multi-level parallelization that includes job level, process level and thread level parallelization, enabled by job scheduling management, message passing interface (MPI) and OpenMP, respectively. It involves job distribution, chunk partition and execution, parallelized iteration for imputation and data concatenation. Due to the design of multi-level parallelization, we can exploit the multi-machine/multi-core architecture to improve the performance of genotype imputation. Results: Experiment results show that our proposed method can outperform the Hadoop-based implementation of genotype imputation. Moreover, we conduct the experiments on supercomputers to evaluate the performance of the proposed method. The evaluation shows that it can significantly shorten the execution time, thus improving the performance for genotype imputation. Conclusion: The proposed multi-level parallelization, when deployed as an imputation as a service, will facilitate bioinformatics researchers in Singapore to conduct genotype imputation and enhance the association study.

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

A high-performance, portable implementation of the MPI message passing interface standard

1996 ◽  
Vol 22 (6) ◽  
pp. 789-828 ◽  
Author(s):  
William Gropp ◽  
Ewing Lusk ◽  
Nathan Doss ◽  
Anthony Skjellum
Keyword(s):  
Message Passing ◽  
High Performance ◽  
Message Passing Interface
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