Recurrent Neural Networks for One Day Ahead Prediction of Stream Flow

Mhammedi, Zakaria; Hellicar, Andrew; Rahman, Ashfaqur; Kasfi, Kasirat Turfi; Smethurst, P. J.

doi:10.1145/3014340.3014345

Cited by 12 publications

(7 citation statements)

References 11 publications

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“…www.nature.com/scientificreports www.nature.com/scientificreports/ RNN. RNNs have proven useful at time series 36 , natural language processing 37 , and bioinformatics 38,39 . In short, these ANNs yield satisfactory results at applications that use serial and connected data sets.…”

Section: Methodsmentioning

confidence: 99%

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Kaya

Öğüdücü

2020

Sci Rep

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Growing metropolitan areas bring rapid urbanization and air pollution problems. As diseases and mortality rates increase because of the air pollution problem, it becomes a necessity to estimate the air pollution density and inform the public to protect the health. Air pollution problem displays contextual characteristics such as meteorological conditions, industrial and technological developments, traffic problem etc. that change from country to country and also from city to city. In this study, we determined PM 10 as the target pollutant and designed a new deep learning based air quality forecasting model, namely DFS (Deep Flexible Sequential). Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before. DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). The proposed model also is capable of generalization with standard and flexible Dropout layers. Through flexible Dropout layer, the model also obtains flexibility to adapt changing window sizes in sequential modelling. Moreover, this model can be applied to other air pollution time series data problems with small modifications on parameters by taking into account the nature of the data set.

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Section: Methodsmentioning

confidence: 99%

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Kaya

Öğüdücü

2020

Sci Rep

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“…Air pollutant concentration at a specific time t is influenced not only by the current conditions, but also by the values at a previous time t. Given this, a recurrent neural network (RNN) is selected, which can generate forecasts with sequential information flow [42]. RNN networks are feed-forward neural networks with cyclic connections between neurons that allow one to transfer information, introducing the output from the previous steps as input for the next step.…”

Section: Lstmmentioning

confidence: 99%

Forecasting Particulate Pollution in an Urban Area: From Copernicus to Sub-Km Scale

Pappa

Kioutsioukis

2021

Atmosphere

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Particulate air pollution has aggravated cardiovascular and lung diseases. Accurate and constant air quality forecasting on a local scale facilitates the control of air pollution and the design of effective strategies to limit air pollutant emissions. CAMS provides 4-day-ahead regional (EU) forecasts in a 10 km spatial resolution, adding value to the Copernicus EO and delivering open-access consistent air quality forecasts. In this work, we evaluate the CAMS PM forecasts at a local scale against in-situ measurements, spanning 2 years, obtained from a network of stations located in an urban coastal Mediterranean city in Greece. Moreover, we investigate the potential of modelling techniques to accurately forecast the spatiotemporal pattern of particulate pollution using only open data from CAMS and calibrated low-cost sensors. Specifically, we compare the performance of the Analog Ensemble (AnEn) technique and the Long Short-Term Memory (LSTM) network in forecasting PM2.5 and PM10 concentrations for the next four days, at 6 h increments, at a station level. The results show an underestimation of PM2.5 and PM10 concentrations by a factor of 2 in CAMS forecasts during winter, indicating a misrepresentation of anthropogenic particulate emissions such as wood-burning, while overestimation is evident for the other seasons. Both AnEn and LSTM models provide bias-calibrated forecasts and capture adequately the spatial and temporal variations of the ground-level observations reducing the RMSE of CAMS by roughly 50% for PM2.5 and 60% for PM10. AnEn marginally outperforms the LSTM using annual verification statistics. The most profound difference in the predictive skill of the models occurs in winter, when PM is elevated, where AnEn is significantly more efficient. Moreover, the predictive skill of AnEn degrades more slowly as the forecast interval increases. Both AnEn and LSTM techniques are proven to be reliable tools for air pollution forecasting, and they could be used in other regions with small modifications.

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“…Recurrent neural networks (RNNs) are more suitable for time series prediction than classical neural networks (Mhammedi et al, 2016). Input vectors of RNN are updated recurrently through the same operation cell, which means that the data retains previous information.…”

Section: Model Concept and Implementationmentioning

confidence: 99%

Temporal convolutional network algorithm for streamflow predictions in a subtropical river

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

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Rainfall-runoff models have a high degree of uncertainty and stochasticity, and the relationship between them is non-linear. Conventional hydrology streamflow prediction models are mostly built for specific watersheds and specific prediction scales, which are poorly promoted and applied. Therefore, in some scenarios, data-driven machine learning predictive models are replacing traditional physical models. Long short-term memory (LSTM) network is a machine learning algorithm for predicting time series and has been applied in the field of streamflow prediction. Temporal convolutional network (TCN) is another machine learning algorithm that is gaining popularity in the field of time series forecasting. LSTM and TCN were implemented in this study to analyse the hourly streamflow prediction for the Nerang River at the Numinbah gauging site, and the predictive accuracy of the models on the test dataset was calculated based on the historical data of the study area. According to the results of the analysis, the TCN model achieved better performance for the hourly streamflow prediction with a coefficient of determination (𝑅𝑅 2 ) of 0.9837 and Nash-Sutcliffe efficiency (𝑁𝑁𝑁𝑁𝑁𝑁) of 0.9829 in the best scenario and the lag time for hourly streamflow generation is about three hours in the study area. Additionally, the maximum predicted lead time is six hours in the study area on the TCN model.In summary, the accuracy of hourly streamflow prediction using the TCN algorithm is of a good level. Moreover, streamflow can be predicted for up to six hours. Finally, this study demonstrates that the novel TCN algorithm has enormous potential for solving streamflow prediction problems in comparison to the LSTM algorithm.

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Recurrent Neural Networks for One Day Ahead Prediction of Stream Flow

Cited by 12 publications

References 11 publications

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Forecasting Particulate Pollution in an Urban Area: From Copernicus to Sub-Km Scale

Temporal convolutional network algorithm for streamflow predictions in a subtropical river

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