Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks

Zaytar, Mohamed Akram; Amrani, Chaker El

doi:10.5120/ijca2016910497

Cited by 109 publications

(25 citation statements)

References 5 publications

(4 reference statements)

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“…In the same year, a model was developed to predict the temperate in Nevada using a deep neural network with stacked denoising auto-encoders with higher accuracy of 97.97% compared to traditional neural networks (94.92%) [35]. In 2016, the multi-stacked deep learning LSTM approach was utilised to forecasting weather parameters temperature, humidity, and wind speed [36]. The author suggested that the model could be used to predict other weather parameters based on the effectiveness and accuracy of the results.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed model is a Single-input single-output and able to produce a state-of-the-art performance for up to 6 h Forecasting the weather of Nevada: a deep learning approach [35] This model accepts four input parameters and predicts one output as temperature. Results indicated that stacked denoising auto-encoder deep learning model predicts accurate long-term temperature Sequence to sequence weather forecasting with long short-term memory recurrent neural networks [36] Multi-stacked LSTMs are used to map sequences of weather values of the same length. Use three input parameters and predict one parameter at a time A deep learning methodology based on bidirectional gated recurrent unit for wind power prediction [62] Contributed the bidirectional gated recurrent network for wind power forecasting.…”

Section: Weather Research and Forecasting (Wrf) Modelmentioning

confidence: 99%

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Deep learning-based effective fine-grained weather forecasting model

Hewage

Trovati

Pereira

et al. 2020

Pattern Anal Applic

130

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It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compare its performance with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method, as well as the well-established weather research and forecasting (WRF) NWP model. More specifically Standard Regression (SR), Support Vector Regression (SVR), and Random Forest (RF) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA), Vector Auto Regression (VAR), and Vector Error Correction Model (VECM) are implemented as the statistical forecasting approaches. Furthermore, Arbitrage of Forecasting Expert (AFE) is implemented as the dynamic ensemble method in this article. Weather information is captured by time-series data and thus, we explore the state-of-art LSTM and TCN models, which is a specialised form of neural network for weather prediction. The proposed deep model consists of a number of layers that use surface weather parameters over a given period of time for weather forecasting. The proposed deep learning networks with LSTM and TCN layers are assessed in two different regressions, namely multi-input multi-output and multi-input single-output. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate weather forecasting up to 12 h.

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Section: Related Workmentioning

confidence: 99%

Section: Weather Research and Forecasting (Wrf) Modelmentioning

confidence: 99%

Deep learning-based effective fine-grained weather forecasting model

Hewage

Trovati

Pereira

et al. 2020

Pattern Anal Applic

130

View full text Add to dashboard Cite

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“…Best results were obtained from the model consisting of 3 hidden layers constituting 300, 500 and 200 neurons. In [18], 15 years of hourly meteorological data for 9 cities in Morocco was analyzed by authors using multi-stacked LSTM to forecast 24 and 72 hours weather data. Missing values were eliminated using forward filling and RMSProp optimizer was used.…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction

Mittal¹,

Sangwan²

2020

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

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“…It is advantageous to train and model meteorological data with time series characteristics and to conduct forecast research using the LSTM. Akram and El [29] used 15 years of hourly weather data to train the multilayer LSTM model and to forecast meteorological conditions out to 24 and 72 hours. ey found that the LSTM can forecast general weather variables with a better accuracy.…”

mentioning

confidence: 99%

A Convection Nowcasting Method Based on Machine Learning

Aifang¹,

Li²,

Cui³

et al. 2020

Advances in Meteorology

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In this study, a convection nowcasting method based on machine learning was proposed. First, the historical data were back-calculated using the pyramid optical flow method. Next, the generated optical flow field information of each pixel and the Red-Green-Blue (RGB) image information were input into the Convolutional Long Short-Term Memory (ConvLSTM) algorithm for training purposes. During the extrapolation process, dynamic characteristics such as the rotation, convergence, and divergence in the optical flow field were also used as predictors to form an optimal nowcasting model. The test analysis demonstrated that the algorithm combined the image feature extraction ability of the convolutional neural network (CNN) and the sequential learning ability of the long short-term memory network (LSTM) model to establish an end-to-end deep learning network, which could deeply extract high-order features of radar echoes such as structural texture, spatial correlation, and temporal evolution compared with the traditional algorithm. Based on learning through the above features, this algorithm can forecast the generation and dissipation trends of convective cells to some extent. The addition of the optical flow information can more accurately simulate nonlinear trends such as the rotation, or merging, or separation of radar echoes. The trajectories of radar echoes obtained through nowcasting are closer to their actual movements, which prolongs the valid forecasting period and improves forecast accuracy.

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Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks

Cited by 109 publications

References 5 publications

Deep learning-based effective fine-grained weather forecasting model

Deep learning-based effective fine-grained weather forecasting model

Big Data Analytics using Deep LSTM Networks: A Case Study for Weather Prediction

A Convection Nowcasting Method Based on Machine Learning

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