Long Term Capability Requirements as derived from the Long Term Requirements Study

Brownlee, James

doi:10.1109/wssc.2010.5730228

Cited by 2 publications

(3 citation statements)

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“…This study aimed to develop a sequence prediction model that performs the best result. There are five steps to establish a simple RNN model (Figure 4) and LSTM model (Figure 5), as follows [24]:…”

Section: Development Of Recurrent Neural Network (Rnns) Modelsmentioning

confidence: 99%

“…The result showed that both LSTM and RNN models fit the sequence data because training loss decreased over time, achieving low error values. A good fit is determined by a training and validation loss that decreases to the point of constancy with a minimal gap between the two ending loss values[24]. Moreover, the loss of the model should be lower on the training set than on the test set.…”

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Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Wongburi,

Park

2023

Water

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Artificial Intelligence (AI) has recently emerged as a powerful tool with versatile applications spanning various domains. AI replicates human intelligence processes through machinery and computer systems, finding utility in expert systems, image and speech recognition, machine vision, and natural language processing (NLP). One notable area with limited exploration pertains to using deep learning models, specifically Recurrent Neural Networks (RNNs), for predicting water quality in wastewater treatment plants (WWTPs). RNNs are purpose-built for handling sequential data, featuring a feedback mechanism. However, standard RNNs may exhibit limitations in accommodating both short-term and long-term dependencies when addressing intricate time series problems. The solution to this challenge lies in adopting Long Short-Term Memory (LSTM) cells, known for their inherent memory management through a ‘forget gate’ mechanism. In general, LSTM architecture demonstrates superior performance. WWTP data represent a historical series influenced by fluctuating environmental conditions. This study employs simple RNNs and LSTM architecture to construct prediction models for effluent parameters, systematically assessing their performance through various training data scenarios and model architectures. The primary objective was to determine the most suitable WWTP dataset model. The study revealed that an epoch setting of 50 and a batch size of 100 yielded the lowest training time and root mean square error (RMSE) values for both RNN and LSTM models. Furthermore, when these models are applied to predict effluent parameters, they exhibit precise RMSE values for all parameters. The study results can be applied to detect potential upsets in WWTP operations.

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Section: Development Of Recurrent Neural Network (Rnns) Modelsmentioning

confidence: 99%

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confidence: 99%

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Wongburi,

Park

2023

Water

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“…Another issue addressed during the preparation was how to deal with the time series data and prepare it correctly for the algorithm. In the case of applications such as neural networks, we have tools for time series, or a suitable type, called long-short term memory (LSTM) networks (Brownlee, 2017). In conventional machine learning methods, especially supervised learning, we will certainly want to see what the importance of each observation term for forecasting is, etc.…”

Section: Creating Shifted Valuesmentioning

confidence: 99%

Kratkoročna prognoza vidljivosti određena metodom slučajne šume

Sládek

2023

Geofizika (Online)

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Accurate visibility forecasting is essential for safe aircraft operations. This study examines how various configurations of the Random Forest model can enhance visibility predictions. Preprocessing techniques are employed, including correlation analysis to identify fundamental relationships in weather observations. Time-series data is transformed into a regular Data Frame to facilitate analysis. This study proposes a classification framework for organizing visibility data and phenomena, which is then used to develop a visibility forecast using the Random Forest method. The study also presents procedures for hyperparameter tuning, feature selection, data balancing, and accuracy evaluation for this dataset. The main outcomes are the Random Forest model parameters for a three-hour visibility forecast, along with an analysis of errors in low visibility forecasts. Additionally, models for one-hour forecasts and visibility forecasting under precipitation are also examined. The resulting models demonstrate a deterministic forecast accuracy of approximately 78%, with a false alarm rate of around 6%, providing a comprehensive overview of the capabilities of the Random Forest model for visibility forecasting. As anticipated, the model demonstrated limitations in accurately simulating fast radiative cooling or abrupt decreases in visibility caused by precipitation. Specifically, in relation to precipitation, the model achieved an accuracy of 79%, yet exhibited a false alarm rate of 19%. Additionally, this method sets a foundation for enhancing prediction accuracy through the inclusion of supplementary forecast data, while its implementation on real-world datasets expands the reach of machine learning techniques to the members of the meteorological community.

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Long Term Capability Requirements as derived from the Long Term Requirements Study

Abstract: Of the 38 Long Term Capability Requirements indentified by NATO in the 2009 Long Term Requirements Study, at least four can be directly applied to waterside security research and technology.

Cited by 2 publications

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Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Prediction of Wastewater Treatment Plant Effluent Water Quality Using Recurrent Neural Network (RNN) Models

Kratkoročna prognoza vidljivosti određena metodom slučajne šume

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