Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5

Sadler, Jeffrey M.; Goodall, Jonathan L.; Behl, Madhur; Morsy, Mohamed M.; Culver, Teresa B.; Bowes, Benjamin D.

doi:10.1016/j.envsoft.2019.07.009

Cited by 64 publications

(36 citation statements)

References 32 publications

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“…Prior modeling studies have documented the performance of real-time control strategies in decreasing flooding magnitude and peak water level approximately by 40% to 70% (Sadler et al, 2019;Wong and Kerkez, 2018). However, these studies, on the one hand, ignore the physical dynamics by a linearizing system or manually set-up the control rules based on experts' experience.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

2020

Science of The Total Environment

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An open-to-public tool called SWMM_FLC was developed for co-simulating fuzzy logic control and hydraulic-hydrologic procedure; 2. A data-driven method was used to train the relationship between inputs and outputs of fuzzy inference system; 3. Genetic algorithm was implemented to improve the fuzzy inference system performance by minifying the deviations between predictions and expectations; 4. SWMM_FLC can be used as an optimization-simulation tool to reduce total flooding volume at downstream urban drainage systems.

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Section: Discussion and Limitationsmentioning

confidence: 99%

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

2020

Science of The Total Environment

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“…One of parallel computerized models is the LSTM model. Parallel computing is a type of computation in which various process calculations can be carried out simultaneously, while the application of parallel computing can run algorithm more quickly in the appearance of the model used in this study [15], [16]. Based on that study, we choose parallel computing models to researched more deeply.…”

Section: Lstm Modelmentioning

confidence: 99%

Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme

Anki

Bustamam²

2021

IJEECS

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Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.

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“…Real-time modeling tool applications like EPANET-RTX [90], LVVWD [91], and EPANET-CPS [92] actually need two steps to finish work: pausing execution and waiting for the new SCADA measurements to reload and to update the boundary condition [93]. The Pausing and Waiting takes typically 10 seconds and 14 minutes, respectively, which makes smart modeling is close to being near real-time process [94].…”

Section: Smartnessmentioning

confidence: 99%

Rethinking the Framework of Smart Water System: A Review

Yang

Sitzenfrei

2020

Water

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Throughout the past years, governments, industries, and researchers have shown increasing interest in incorporating smart techniques, including sensor monitoring, real-time data transmitting, and real-time controlling into water systems. However, the design and construction of such a smart water system are still not quite standardized for massive applications due to the lack of consensus on the framework. The major challenge impeding wide application of the smart water network is the unavailability of a systematic framework to guide real-world design and deployment. To address this challenge, this review study aims to facilitate more extensive adoption of the smart water system, to increase effectiveness and efficiency in real-world water system contexts. A total of 32 literature pieces including 1 international forum, 17 peer-reviewed papers, 10 reports, and 4 presentations that are directly related to frameworks of smart water system have been reviewed. A new and comprehensive smart water framework, including definition and architecture, was proposed in this review paper. Two conceptual metrics (smartness and cyber wellness) were defined to evaluate the performance of smart water systems. Additionally, three pieces of future research suggestions were discussed, calling for broader collaboration in the community of researchers, engineers, and industrial and governmental sectors to promote smart water system applications.

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Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5

Cited by 64 publications

References 32 publications

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme

Rethinking the Framework of Smart Water System: A Review

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