Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal
Contemporarily, one of the most pressing concerns is reliable and rapid weather forecasting. In Nepal, the Department of Hydrology and Meteorological uses a numerical modeling approach to forecast the weather, which is tardy and requires high-end equipment to process the information, so a deep learn...
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Tishk International University
2024-06-01
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| Series: | Eurasian Journal of Science and Engineering |
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| Online Access: | https://eajse.tiu.edu.iq/index.php/eajse/article/view/14 |
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| author | Supath Dhital Kapil Lamsal Sulav Shrestha Umesh Bhurtyal |
| author_facet | Supath Dhital Kapil Lamsal Sulav Shrestha Umesh Bhurtyal |
| author_sort | Supath Dhital |
| collection | DOAJ |
| description | Contemporarily, one of the most pressing concerns is reliable and rapid weather forecasting. In Nepal, the Department of Hydrology and Meteorological uses a numerical modeling approach to forecast the weather, which is tardy and requires high-end equipment to process the information, so a deep learning approach will be the best alternative. This project aims to forecast the next 2-hour Precipitation and Air Temperature for Pokhara Domestic Airport meteorological station and the next day's Precipitation, Maximum and Minimum Air Temperature forecast for Lumle, Begnas, and Lamachaur meteorological station, total of four meteorological stations of the Kaski District, Nepal using Long Short-Term Memory (LSTM): a Recurrent Neural Network (RNN) and deploy the outputs through the web portal. The four hourly parameters: Rainfall, Relative Humidity (R.H), Wind Speed, and Air Temperature, were used for modeling the airport station forecast, whereas Rainfall, Relative Humidity (R.H), Maximum and Minimum Temperature were used for modeling the Begnas and Lumle station forecast and only Precipitation data was used for Lamachaur station. Averaging and linear interpolation techniques were used to fill out the missing values and outliers were detected using Box Plot and replaced with threshold value for each parameter. Stochastic Gradient Descent and Adam optimizer are used to optimize the LSTM model. Among all the models prepared, Root Mean Square Error (RMSE) values range from 0.58 to 4.08 for the precipitation model and from 0.16 to 0.82 for the air temperature model, and Mean Absolute Error (MAE) values range from 0.21 to 2.87 for the precipitation model and from 0.12 to 0.64 for air temperature model were the values of the final model that indicates better accuracy for air temperature. The R² values range from 0.89 to 0.99, indicating the train and test data were fitted to the model really well. |
| format | Article |
| id | doaj-art-911096452044496fa7993d6e7abae017 |
| institution | Kabale University |
| issn | 2414-5629 2414-5602 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tishk International University |
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| series | Eurasian Journal of Science and Engineering |
| spelling | doaj-art-911096452044496fa7993d6e7abae0172025-02-05T12:45:22ZengTishk International UniversityEurasian Journal of Science and Engineering2414-56292414-56022024-06-011021633https://doi.org/10.23918/eajse.v10i2p02Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, NepalSupath Dhital0https://orcid.org/0000-0002-9535-8544Kapil Lamsal1https://orcid.org/0009-0002-7630-9605Sulav Shrestha2https://orcid.org/0009-0001-0203-9262Umesh Bhurtyal3https://orcid.org/0009-0009-9154-9683Department of Geography and Environment Studies, The University of Alabama, Tuscaloosa AL-USADepartment of Geomatics Engineering, Faculty of Engineering, Tribhuvan University, Pokhara-NepalDepartment of Geomatics Engineering, Faculty of Engineering, Tribhuvan University, Pokhara-NepalDepartment of Geomatics Engineering, Faculty of Engineering, Tribhuvan University, Pokhara-Nepal Contemporarily, one of the most pressing concerns is reliable and rapid weather forecasting. In Nepal, the Department of Hydrology and Meteorological uses a numerical modeling approach to forecast the weather, which is tardy and requires high-end equipment to process the information, so a deep learning approach will be the best alternative. This project aims to forecast the next 2-hour Precipitation and Air Temperature for Pokhara Domestic Airport meteorological station and the next day's Precipitation, Maximum and Minimum Air Temperature forecast for Lumle, Begnas, and Lamachaur meteorological station, total of four meteorological stations of the Kaski District, Nepal using Long Short-Term Memory (LSTM): a Recurrent Neural Network (RNN) and deploy the outputs through the web portal. The four hourly parameters: Rainfall, Relative Humidity (R.H), Wind Speed, and Air Temperature, were used for modeling the airport station forecast, whereas Rainfall, Relative Humidity (R.H), Maximum and Minimum Temperature were used for modeling the Begnas and Lumle station forecast and only Precipitation data was used for Lamachaur station. Averaging and linear interpolation techniques were used to fill out the missing values and outliers were detected using Box Plot and replaced with threshold value for each parameter. Stochastic Gradient Descent and Adam optimizer are used to optimize the LSTM model. Among all the models prepared, Root Mean Square Error (RMSE) values range from 0.58 to 4.08 for the precipitation model and from 0.16 to 0.82 for the air temperature model, and Mean Absolute Error (MAE) values range from 0.21 to 2.87 for the precipitation model and from 0.12 to 0.64 for air temperature model were the values of the final model that indicates better accuracy for air temperature. The R² values range from 0.89 to 0.99, indicating the train and test data were fitted to the model really well.https://eajse.tiu.edu.iq/index.php/eajse/article/view/14weather forecastdeep learninglong short-term memory (lstm)meteorological dataprecipitationair temperature |
| spellingShingle | Supath Dhital Kapil Lamsal Sulav Shrestha Umesh Bhurtyal Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal Eurasian Journal of Science and Engineering weather forecast deep learning long short-term memory (lstm) meteorological data precipitation air temperature |
| title | Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal |
| title_full | Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal |
| title_fullStr | Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal |
| title_full_unstemmed | Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal |
| title_short | Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal |
| title_sort | forecasting weather using deep learning from the meteorological stations data a study of different meteorological stations in kaski district nepal |
| topic | weather forecast deep learning long short-term memory (lstm) meteorological data precipitation air temperature |
| url | https://eajse.tiu.edu.iq/index.php/eajse/article/view/14 |
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