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Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

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%).

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

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%).

Mapping Irrigation Networks with Geographical Information Systems Using Satelite Imagery Data: A Case of Brebes Regency, Indonesia Aulia Azhar Abdurachman; Muhammad Fahmi Arsyad; Edi Abdurahman; Togar Alam Napitupulu; Nilo Legowo
CommIT (Communication and Information Technology) Journal Vol. 13 No. 1 (2019): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5075

Water resources are important factors in food production. Those are very vital and strategic to meet food needs and food security. As water is scarce both in terms of volume and distribution throughout the year, reliable water management is needed. To support this water management, the accurate data is needed. However, the complete tabular data is not enough. It is because the existing tabular data does not provide the various activities and events based on time and place spatially and detail enough for planning purposes at the sub-district level. The researchers use high-resolution satellite imagery data that have been pre-processed with the geometric and radiometric corrections. They are used as one of the layers in the working map, so it is easier to provide the depiction of irrigation network objects, to find out the location of rice fields that have not been irrigated and the location of damaged irrigation networks. The depiction of the working map can also be used to map irrigation networks and their network conditions. Through this work, it has been shown that the researchers can map irrigation networks in detail for operational planning at a sub-district level with the help of technology, in particular for developing countries that is difficult or even impossible to do in the past.

Weather Forecasting Using Merged Long Short-term Memory Model Afan Galih Salman; Yaya Heryadi; Edi Abdurahman; Wayan Suparta
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

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%).

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