Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method
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...
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Diponegoro University; Association of Indonesian Coastal Management Experts
2021-09-01
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| Series: | Ilmu Kelautan |
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| Online Access: | https://ejournal.undip.ac.id/index.php/ijms/article/view/34602 |
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| author | Anisa Nur Azizah Dian C.R. Novitasari Putroue Keumala Intan Fajar Setiawan Ghaluh Indah Permata Sari |
| author_facet | Anisa Nur Azizah Dian C.R. Novitasari Putroue Keumala Intan Fajar Setiawan Ghaluh Indah Permata Sari |
| author_sort | Anisa Nur Azizah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e05c9e13619744ae896adf55a59a95bc |
| institution | Kabale University |
| issn | 0853-7291 2406-7598 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | Diponegoro University; Association of Indonesian Coastal Management Experts |
| record_format | Article |
| series | Ilmu Kelautan |
| spelling | doaj-art-e05c9e13619744ae896adf55a59a95bc2025-08-20T03:29:14ZengDiponegoro University; Association of Indonesian Coastal Management ExpertsIlmu Kelautan0853-72912406-75982021-09-0126320721410.14710/ik.ijms.26.3.207-21419163Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network MethodAnisa Nur Azizah0Dian C.R. Novitasari1Putroue Keumala Intan2Fajar Setiawan3Ghaluh Indah Permata Sari4Department of Mathematics, UIN Sunan Ampel, IndonesiaDepartment of Mathematics, UIN Sunan Ampel, IndonesiaDepartment of Mathematics, UIN Sunan Ampel, IndonesiaMeteorogical, Climatological and Geophysics Agency Surabaya, IndonesiaDepartment of Computer Science and Information Engineering, National Taiwan University of Science and Technology, TaiwanSalinity 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.https://ejournal.undip.ac.id/index.php/ijms/article/view/34602salinityevaporationprecipitationtime seriesbackpropagation |
| spellingShingle | Anisa Nur Azizah Dian C.R. Novitasari Putroue Keumala Intan Fajar Setiawan Ghaluh Indah Permata Sari Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method Ilmu Kelautan salinity evaporation precipitation time series backpropagation |
| title | Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method |
| title_full | Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method |
| title_fullStr | Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method |
| title_full_unstemmed | Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method |
| title_short | Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method |
| title_sort | prediction of salinity based on meteorological data using the backpropagation neural network method |
| topic | salinity evaporation precipitation time series backpropagation |
| url | https://ejournal.undip.ac.id/index.php/ijms/article/view/34602 |
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