Learning Hierarchical Weather Data Representation for Short-Term Weather Forecasting Using Autoencoder and Long Short-Term Memory Models

2019 ◽  
pp. 373-384 ◽  
Author(s):  
Yaya Heryadi
Keyword(s):  
Short Term Memory ◽  
Weather Forecasting ◽  
Data Representation ◽  
Memory Models ◽  
Weather Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Deep Network based on Long Short-Term Memory for Time Series Prediction of Microclimate Data inside the Greenhouse

2020 ◽  
Vol 19 (02) ◽  
pp. 2050013
Author(s):  
Sawsan Morkos Gharghory
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Data ◽  
Short Term Memory ◽  
Weather Data ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.

scholarly journals Weather Forecasting Using Merged Long Short-term Memory Model

2018 ◽  
Vol 7 (3) ◽  
pp. 377-385 ◽  
Author(s):  
Afan Galih Salman ◽  
Yaya Heryadi ◽  
Edi Abdurahman ◽  
Wayan Suparta
Keyword(s):  
Short Term Memory ◽  
Weather Forecasting ◽  
Human Life ◽  
Predictor Variable ◽  
Dew Point ◽  
Memory Model ◽  
Short Term ◽  
Research Attention ◽  
Term Memory ◽  
Long Short Term Memory

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

Long Short-Term Memory Networks for Real-Time Runoff Forecasting using Remotely Sensed Data

2021 ◽  
Author(s):  
Paul Muñoz ◽  
David F. Muñoz ◽  
Johanna Orellana-Alvear ◽  
Hamed Moftakhari ◽  
Hamid Moradkhani ◽  
...  
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Data Representation ◽  
Tropical Andes ◽  
Short Term ◽  
Modeling Framework ◽  
Term Memory ◽  
Runoff Forecasting ◽  
Long Short Term Memory ◽  
Hydrological Applications

<p>Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been explored in hydrological applications such as runoff forecasting. An aditional challenge to the forecasting task arises from the uncertainties generated when using readily-available Remote Sensing (RS) imagery aimed to overcome lack of in-situ data describing the runoff governing processes. Here, we proposed a runoff forecasting framework for a 300-km<sup>2 </sup>mountain catchment located in the tropical Andes of Ecuador. The framework consists on real-time data acquisition, preprocessing and runoff forecasting for lead times between 1 and 12 hours. LSTM models were fed with 18 years of hourly runoff, and precipitation data from the novel PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now) product. Model efficiencies according to the NSE metric ranged from 0.959 to 0.554, for the 1- to 12-hour models, respectively. Considering that the concentration time of the catchment is approximately 4 hours, the proposed framework becomes a useful tool for delivering runoff forecasts to decision makers, stakeholders and the public. This study has shown the suitability of using the PDIR-Now product in a LSTM-modeling framework for real-time hydrological applications. Future endeavors must focus on improving data representation and data assimilation through feature engineering strategies.</p><p><strong>Keywords: </strong>Long Short-Term Memory; PDIR-Now; Hydroinformatics; Runoff forecasting; Tropical Andes</p>

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