Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed

Mbuh, Mbongowo J.; Mbih, Richard Achia; Wendi, Comfort

doi:10.1080/02723646.2018.1507339

Cited by 27 publications

(7 citation statements)

References 34 publications

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“…• Comparison: If compare the scrum methodology, with the methodology RUP (rational unified process) [37], can say, that the methodology RUP (rational unified process), is rigid, meaning that if the customer wants a change, it is very difficult to make it, since you follow the plan until the end of the project development, whereas in the Scrum, this is possible, because, scrum is flexible to change, another comparison, you can make, The reason for this is that, in the methodology RUP (rational unified process), the project is conceived as one, while in scrum the project is divided into parts. It can also be said that in the methodology, the deliverable is at the end of the entire project development, while in scrum the delivery is constant [38]. This comparison of Scrum and RUP can be seen in Table V.…”

Section: About the Methodologymentioning

confidence: 96%

Augmented Reality: Prototype for the Teaching-Learning Process in Peru

Morales¹,

Andrade–Arenas²,

Delgado³

et al. 2022

IJACSA

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In recent years, augmented reality is playing an important role in the world of mobile technology, since it is a way to facilitate the teaching-learning processes, this easy teachinglearning process generates a great contribution in companies, either creating opportunities or changing the way in which companies approach and interact with their end customers, this can mean a remarkable growth of the organization, that is why in this work an augmented reality prototype has been made using the methodology Scrum at the University of Sciences and Humanities of Lima-Peru, but with a focus on the nursing career. Knowing that the problem is the limited learning that students acquire in the classrooms, for which, we want to make use of augmented reality, so that this improves the form of education that is provided to university students. The result obtained, from developing the case study, was an augmented reality prototype for the improvement of education at the University of Sciences and Humanities of Lima-Peru, which shows a virtual model (it depends on the image shown), able to interact with the user, making it attractive and motivating for the student, this prototype was achieved, using Unity (3D development platform, Vuforia (augmented reality software development kit), Microsoft Visual Studio (integrated development environment), the Scrum Methodology (Scrum Pillars, Product Backlog, Product Backlog Estimation, Speed, Backlog Prioritization and Sprint Planning) and the C# Language (C Sharp).

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Section: About the Methodologymentioning

confidence: 96%

Augmented Reality: Prototype for the Teaching-Learning Process in Peru

Morales¹,

Andrade–Arenas²,

Delgado³

et al. 2022

IJACSA

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“…Existing water quality prediction models can be divided into two types: mechanistic and non-mechanistic models [7,8] . Mechanistic models, such as the Streeter Phelps (S-P) model [9][10][11][12] , QUAL2E model [13][14][15] , Water Quality Analysis Simulation Program (WASP) [15][16][17] , Soil and Water Assessment Tool (SWAT) [18][19][20] , etc., are based on physical and chemical principles and require a large amount of basic and long-term monitoring data. However, such data are often unavailable or incomplete in practice, which limits the applicability and accuracy of mechanistic models.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

pang,

Luo,

et al. 2023

Preprint

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The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected and organized real-time water quality monitoring data from four water quality monitoring stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. To reduce noise interference in historical monitoring data, we applied a wavelet packet denoising (WPD) technique. Subsequently, we developed a single-factor water quality prediction model based on Long Short-Term Memory (LSTM), focusing on two key factors of water quality degradation: chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 hours and 3 days. In the 12-hour prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, with an average decrease of 61%; the RMSE values of COD predictions in the four monitoring sections decreased by 18% to 51%, with an average decrease of 29%. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, with an average decrease of 65%; the RMSE values of COD predictions in the four monitoring sections decreased by 50% to 69%, with an average decrease of 60%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

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“…In cases where the station network is sparse and the spatial heterogeneity of COD is high, it can be challenging to obtain accurate spatial information of COD using interpolation-based methods. Another basic approach required to solve the water pollution problem is the modeling of water quality changes by developing some mathematical models [ 14 , 15 ]. These process-based models have the ability of simulating and predicting complex processes in water ecosystems, identifying the behavior of pollutants, and recognizing the spatial distribution of water quality parameters [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Spatial Estimation of Water Quality Using an Optimal Virtual Sensor Network and In Situ Observations: A Case Study of Chemical Oxygen Demand

Zhao

2023

Sensors

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Accurate water quality estimation is important for water environment monitoring and water resource management and has emerged as a pivotal aspect of ecological rehabilitation and sustainable development. However, due to the strong spatial heterogeneity of water quality parameters, it is still challenging to obtain highly accurate spatial patterns of them. Taking chemical oxygen demand as an example, this study proposes a novel estimation method for generating highly accurate chemical oxygen demand fields in Poyang Lake. Specifically, based on the different water levels and monitoring sites in Poyang Lake, an optimal virtual sensor network was first established. A Taylor expansion-based method with integration of spatial correlation and spatial heterogeneity was developed by considering environmental factors, the optimal virtual sensor network, and existing monitoring stations. The proposed approach was evaluated and compared with other approaches using a leave-one cross-validation process. Results show that the proposed method exhibits good performance in estimating chemical oxygen demand fields in Poyang Lake, with mean absolute error improved by 8% and 33%, respectively, on average, when compared with classical interpolators and remote sensing methods. In addition, the applications of virtual sensors improve the performance of the proposed method, with mean absolute error and root mean squared error values reduced by 20% to 60% over 12 months. The proposed method provides an effective tool for estimating highly accurate spatial fields of chemical oxygen demand concentrations and could be applied to other water quality parameters.

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Water quality modeling and sensitivity analysis using Water Quality Analysis Simulation Program (WASP) in the Shenandoah River watershed

Cited by 27 publications

References 34 publications

Augmented Reality: Prototype for the Teaching-Learning Process in Peru

Augmented Reality: Prototype for the Teaching-Learning Process in Peru

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

A New Method for Spatial Estimation of Water Quality Using an Optimal Virtual Sensor Network and In Situ Observations: A Case Study of Chemical Oxygen Demand

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