Single Layer &amp; Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting

Salman, Afan Galih; Heryadi, Yaya; Abdurahman, Edi; Suparta, Wayan

doi:10.1016/j.procs.2018.08.153

Cited by 173 publications

(63 citation statements)

References 3 publications

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“…ANN can incorporate complex nonlinear relationships between the concentration of air pollutants and metrological variables. Various ANN structures have been developed to predict air pollution over different study areas, such as neuro-fuzzy neural network (NFNN) [30], Bayesian neural network [31] and Recurrent neural network (RNN) [32,33]. RNN has been applied in many studies involving time-series prediction, such as traffic flow prediction [34] and wind power prediction [35].…”

Section: Air Pollutionmentioning

confidence: 99%

See 1 more Smart Citation

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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Time-series is a sequence of observations that are taken sequentially over time. Modelling a system that generates a future value from past observations is considered as time-series forecasting system. Recurrent neural network is a machine learning method that is widely used in the prediction of future values. Due to variant improvements on recurrent neural networks, choosing of the best model for better prediction generation is dependent on problem domain and model design characteristics. Ensemble forecasting is more accurate than single model due to the combination of more than one model for forecasting. Designing an ensemble model of recurrent neural network for time-series forecasting applications would enhance prediction accuracy and improve performance. This paper highlights some of the challenges that are faced by the design of the ensemble model of different recurrent neural network versions, and surveys some of the most relevant works in order to give a direction of how to conduct ensemble learning research in the future. Based on the reviewed literature, we propose a framework for time-series forecasting based on the using of ensemble technique.

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Section: Air Pollutionmentioning

confidence: 99%

“…Long Short-Term Memory Unit (LSTM), is a state-of-the-art model of RNN that is recently used to predict air quality [14,15]. Many variants of RNN have been developed with different characteristics.…”

Section: Introductionmentioning

confidence: 99%

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Surakhi¹,

Serhan²,

Salah³

2020

Adv. sci. technol. eng. syst. j.

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show abstract

“…Afan Galih Salman proposed a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport by both single layer LSTM model and multi layers LSTM model. By using temperature, dew point, humidity and visibility from all over the world weather stations, the visibility at the airport is predicted [7]. Adding intermediate variable singal into LSTM memory block, the proposed model is extended LSTM model.…”

Section: A Related Workmentioning

confidence: 99%

Cloud and IoT Based Temperature Prediction System for a Greenhouse Using Multivariate Convolutional Long Short Term Memory Network

Oo¹,

Phyu²

2020

IJMLC

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“…The first layer contains the weight vectors that are attained from the input layer. Every layer receives weight from the previous layer [29]. RNNs are designed to capture temporal contextual information along time series data.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

2019

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Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting

Cited by 173 publications

References 3 publications

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges

Cloud and IoT Based Temperature Prediction System for a Greenhouse Using Multivariate Convolutional Long Short Term Memory Network

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

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