Prediction of salinity based on meteorological data using the backpropagation neural network method

Azizah, Anisa Nur and Novitasari, Dian C. R. and Intan, Putroue Keumala and Setiawan, Fajar and Sari, Ghaluh Indah Permata (2021) Prediction of salinity based on meteorological data using the backpropagation neural network method. ILMU KELAUTAN: Indonesian Journal of Marine Sciences, 26 (3). pp. 207-214. ISSN 0853-7291; 2406-7598

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Abstract

Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and precipitation (rainfall). One way to find out the salinity level is by taking seawater samples, which requires a long time and costs a lot. In this study, the salinity level of seawater can be predicted by utilizing time series data patterns from evaporation and precipitation using artificial neural network learning, namely the backpropagation neural network. The evaporation and precipitation data used were derived from the ECMWF dataset, while the salinity data were derived from NOAA where each data was taken at the coordinate point of 9,625 113,625 in the south of Java island. Seawater salinity, evaporation, and precipitation data were formed into a 7- day time series data. This study conducted several backpropagation architectural experiments, that is the learning rate, hidden layer, and the number of nodes in the hidden layer to obtain the best results. The results of the seawater salinity prediction were obtained at a MAPE value of 2.063% with a model architecture using 14 input layers, 2 hidden layers with 10 nodes and 2 nodes, 1 output layer, and a learning rate of 0.7. Predicted sea water salinity data ranging from 33 to 35 ppt. Therefore, the prediction system for seawater salinity using the backpropagation method can be said to be good in providing information about the salinity level of sea water on the island of Java.

Item Type: Article
Additional Information: https://ejournal.undip.ac.id/index.php/ijms/article/view/34602/0
Creators:
Creators
Email
NIDN
Azizah, Anisa Nur
-
-
Novitasari, Dian C. R.
diancrini@uinsby.ac.id
2024118502
Intan, Putroue Keumala
puput.in@gmail.com
0728058802
Setiawan, Fajar
fajar_404@yahoo.com
2006058402
Sari, Ghaluh Indah Permata
-
-
Uncontrolled Keywords: Salinity; evaporation; precipitation; time series; backpropagation
Subjects: 09 ENGINEERING > 0901 Aerospace Engineering > 090108 Satellite, Space Vehicle and Missile Design and Testing
10 TECHNOLOGY > 1005 Communications Technologies > 100505 Microwave and Millimetrewave Theory and Technology
Divisions: Fakultas Sains dan Teknologi > Prodi Matematika
Depositing User: Samidah Nurmayuni
Date Deposited: 16 Jun 2022 07:17
Last Modified: 16 Jun 2022 07:17
URI: http://repository.uinsby.ac.id/id/eprint/2551

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