Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal
DOI:
https://doi.org/10.23918/eajse.v10i2p02Keywords:
Weather Forecast, Deep Learning, Long Short-Term Memory (LSTM), Meteorological Data, Precipitation, Air TemperatureAbstract
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.
References
[1] R.B. Stull, “An introduction to boundary layer meteorology”. Vol. 13. Springer Science & Business Media, 2012. Accessed: Mar. 28, 2024.
[2] “Weather and atmosphere | National Oceanic and Atmospheric Administration.” Accessed: Mar. 28, 2024. [Online]. Available: https://www.noaa.gov/education/resource-collections/weather-atmosphere
[3] “DHM.” Accessed: Mar. 28, 2024. [Online]. Available: https://www.dhm.gov.np/pages/about-us
[4] R. Karki, “Status of automatic weather stations in Nepal and comparison of air temperature and precipitation data between automatic weather station and manual observation,” 2010, Accessed: Mar. 28, 2024.
[5] P. A. Kucera et al., “Precipitation from Space: Advancing Earth System Science,” Bulletin of the Meteorological Society, vol. 94, no. 3, pp. 365–375, Mar. 2013, https://doi.org/10.1175/BAMS-D-11-00171.1
[6] H. Wu et al., “Evaluation of global flood detection using satellite-based rainfall and a hydrologic model,” Journal of Hydrometeorology, 2012, Accessed: Mar. 28, 2024. https://doi.org/10.1175/JHM-D-11-087.1
[7] B. Branko, “Geopolitics of climate change: a review,” Thermal Science, vol. 16, no. 3, pp. 629–654, 2012, https://doi.org/10.2298/TSCI120202127B
[8] Z. L. Li et al., “Satellite-derived land surface temperature: Current status and perspectives,” Remote Sensing Environment, vol. 131, pp. 14–37, Apr. 2013, https://doi.org/10.1016/j.rse.2012.12.008
[9] W. W. Immerzeel, L. P. H. Van Beek, and M. F. P. Bierkens, “Climate change will affect the asian water towers,” Science (1979), vol. 328, no. 5984, pp. 1382–1385, Jun. 2010, https://doi.org/10.1126/science.1183188
[10] R. E. Abdel-Aal, “Hourly temperature forecasting using abductive networks,” Engineering Applications of Artificial Intelligence, vol. 17, no. 5, pp. 543–556, Aug. 2004, https://doi.org/10.1016/j.engappai.2004.04.002
[11] C. Penland, L. Matrosova.-, “Prediction of tropical Atlantic Sea surface temperatures using linear inverse modeling,” Journal of Climate, 1998, https://doi.org/10.1175/1520-0442(1998)011%3C0483:POTASS%3E2.0.CO;2
[12] C. Penland, T. Magorian, “Prediction of Niño 3 sea surface temperatures using linear inverse modeling,” Journal of Climate, 1993, Accessed: Mar. 28, 2024. https://doi.org/10.1175/1520-0442(1993)006%3C1067:PONSST%3E2.0.CO;2
[13] K. P. Moustris, K. P. Moustris, I. K. Larissi, P. T. Nastos, and A. G. Paliatsos, “Precipitation forecast using artificial neural networks in specific regions of Greece,” Springer, Water Resources Management, vol. 25, no. 8, pp. 1979–1993, Jun. 2011, https://doi.org/10.1007/s11269-011-9790-5
[14] B. T. Pham et al., “Development of advanced artificial intelligence models for daily rainfall prediction,” Atmospheric Research, vol. 237, Jun. 2020, https://doi.org/10.1016/j.atmosres.2020.104845
[15] A. Kusiak, X. Wei, A. P. Verma, and E. Roz, “Modeling and prediction of rainfall using radar reflectivity data: A data-mining approach,” IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4, pp. 2337–2342, 2013, https://doi.org/10.1109/TGRS.2012.2210429
[16] K. K. Chowdari, R. Girisha, and K. C. Gouda, “A study of rainfall over India using data mining,” 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2015, pp. 44–47, Jun. 2016, https://doi.org/10.1109/ERECT.2015.7498985
[17] V. P. Tharun, R. Prakash, and S. R. Devi, “Prediction of Rainfall Using Data Mining Techniques,” Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018, pp. 1507–1512, Sep. 2018, https://doi.org/10.1109/ICICCT.2018.8473177
[18] C. Z. Basha, N. Bhavana, P. Bhavya, and V. Sowmya, “Rainfall Prediction using Machine Learning Deep Learning Techniques,” Proceedings of the International Conference on Electronics and Sustainable Communication Systems, ICESC 2020, pp. 92–97, Jul. 2020, https://doi.org/10.1109/ICESC48915.2020.9155896
[19] C. M. Liyew and H. A. Melese, “Machine learning techniques to predict daily rainfall amount,” Journal of Big Data, vol. 8, no. 1, pp. 1–11, Dec. 2021, https://doi.org/10.1186/s40537-021-00545-4
[20] P. Hewage, A. Behera, M. Trovati, and E. Pereira, “Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data,” IFIP International Conference on Artificial Intelligence Applications and Innovations, vol. 559, pp. 382–390, 2019, https://doi.org/10.1007/978-3-030-19823-7_32
[21] J. Kang et al., “Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China,” Atmosphere, vol. 11, no. 3, p. 246, Feb. 2020, https://doi.org/10.3390/atmos11030246
[22] C. J. Gamboa-Villafruela, J. C. Fernández-Alvarez, M. Márquez-Mijares, A. Pérez-Alarcón, and A. J. Batista-Leyva, “Convolutional LSTM Architecture for Precipitation Nowcasting Using Satellite Data,” Environmental Sciences Proceedings 2021, Vol. 8, Page 33, vol. 8, no. 1, p. 33, Jun. 2021, https://doi.org/10.3390/atmos11030246
[23] A. N. Caseri, L. B. Lima Santos, and S. Stephany, “A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil,” Artificial Intelligence in Geosciences, vol. 3, pp. 8–13, Dec. 2022, https://doi.org/10.1016/j.aiig.2022.06.001
[24] B. Ustaoglu, H. K. Cigizoglu, and M. Karaca, “Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods,” Meteorological Applications, vol. 15, no. 4, pp. 431–445, Dec. 2008, https://doi.org/10.1002/met.83
[25] M. Murat, I. Malinowska, M. Gos, and J. Krzyszczak, “Forecasting daily meteorological time series using ARIMA and regression models,” International Agrophysics, vol. 32, pp. 253–264, 2018, http://dx.doi.org/10.1515/intag-2017-0007
[26] M. L. Lin, C. W. Tsai, and C. K. Chen, “Daily maximum temperature forecasting in changing climate using a hybrid of Multi-dimensional Complementary Ensemble Empirical Mode Decomposition and Radial Basis Function Neural Network,” Journal of Hydrology: Regional Studies, vol. 38, p. 100923, Dec. 2021, https://doi.org/10.1016/j.ejrh.2021.100923
[27] D. Shah, “Short term temperature forecasting using LSTMS, and CNN,” 2021, Accessed: Mar. 28, 2024. https://digitalcommons.njit.edu/theses/1840
[28] D. Carrión et al., “A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements,” Environmental Research, vol. 200, p. 111477, Sep. 2021, https://doi.org/10.1016/j.envres.2021.111477
[29] J. Hou, Y. Wang, J. Zhou, and Q. Tian, “Prediction of hourly air temperature based on CNN–LSTM,” Geomatics, Natural Hazards and Risk, vol. 13, no. 1, pp. 1962–1986, Dec. 2022, https://doi.org/10.1080/19475705.2022.2102942
[30] K. Huang, W. Liu, Y. Li, B. Vucetic, “To retransmit or not: Real-time remote estimation in wireless networked control,” IEEE International Conference on Communications (ICC), 2019, https://doi.org/10.1109/ICC.2019.8761710
[31] D. Ji et al., “Assessing Parameter Importance of the Weather Research and Forecasting Model Based on Global Sensitivity Analysis Methods,” Journal of Geophysical Research: Atmospheres, vol. 123, no. 9, pp. 4443–4460, May 2018, https://doi.org/10.1002/2017JD027348
[32] J. K. Lazo, H. R. Hosterman, J. M. Sprague-Hilderbrand, and J. E. Adkins, “Impact-Based Decision Support Services and the Socioeconomic Impacts of Winter Storms,” Bulletin of American Meteorology Society, vol. 101, no. 5, pp. E626–E639, May 2020, https://doi.org/10.1175/BAMS-D-18-0153.1.
[33] M. M. P. Ramos, C. L. Del Alamo, and R. A. Zapana, “Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11678 LNCS, pp. 542–553, 2019, https://doi.org/10.1007/978-3-030-29888-3_44
[34] L. Ni et al., “Streamflow and rainfall forecasting by two long short-term memory-based models,” Journal of Hydrology, vol. 583, p. 124296, Apr. 2020, https://doi.org/10.1016/j.jhydrol.2019.124296
[35] K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Trans Neural Network and Learning System, vol. 28, no. 10, pp. 2222–2232, Oct. 2017, https://doi.org/10.1109/TNNLS.2016.2582924
[36] Y. Liu, D. Hou, J. Bao, and Y. Qi, “Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network,” Proceedings - 2017 14th Web Information Systems and Applications Conference, WISA 2017, vol. 2018-January, pp. 305–310, Jul. 2017, https://doi.org/10.1109/WISA.2017.25
[37] J. Wilson, Y. Feng, M. Chen, R.D. Roberts, “Nowcasting challenges during the Beijing Olympics: Successes, failures, and implications for future nowcasting systems,” Weather and Forecasting, 2010, https://doi.org/10.1175/2010WAF2222417.1
[38] M. Buehner, D. Jacques, “Non-Gaussian deterministic assimilation of radar-derived precipitation accumulations,” Monthly Weather Review, 2020, https://doi.org/10.1175/MWR-D-19-0199.1
[39] J. Sun, “Convective‐scale assimilation of radar data: progress and challenges,” Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 131, no. 613, pp. 3439–3463, Jan. 2005, https://doi.org/10.1256/qj.05.149
[40] K. U. Jaseena and B. C. Kovoor, “Deterministic weather forecasting models based on intelligent predictors: A survey,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3393–3412, Jun. 2022, https://doi.org/10.1016/j.jksuci.2020.09.009
[41] K. Basnet, A. Shrestha, P. C. Joshi, and N. Pokharel, “Analysis of Climate Change Trend in the Lower Kaski District of Nepal,” Himalayan Journal of Applied Science and Engineering, vol. 1, no. 1, pp. 11–22, Dec. 2020, https://doi.org/10.3126/hijase.v1i1.33536
[42] “World weather, climate and accurate forecast information.” Accessed: Mar. 28, 2024. [Online]. Available: https://weather-and-climate.com/
[43] L. Ma, X. Gu, and B. Wang, “Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network,” Information, vol. 8, no. 2, p. 60, May 2017, https://doi.org/10.3390/info8020060
[44] M. S. Pathan, J. Wu, Y. H. Lee, J. Yan, and S. Dev, “Analyzing the Impact of Meteorological Parameters on Rainfall Prediction,” 2021 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), USNC-URSI 2021 - Proceedings, pp. 100–101, 2021, https://doi.org/10.23919/USNC-URSI51813.2021.9703664
[45] S. Siami-Namini, N. Tavakoli, and A. Siami Namin, “A Comparison of ARIMA and LSTM in Forecasting Time Series,” Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, pp. 1394–1401, Jul. 2018, https://doi.org/10.1109/ICMLA.2018.00227
[46] H. Hewamalage, C. Bergmeir, and K. Bandara, “Recurrent Neural Networks for Time Series Forecasting: Current status and future directions,” International Journal of Forecasting, vol. 37, no. 1, pp. 388–427, Jan. 2021, https://doi.org/10.1016/j.ijforecast.2020.06.008
[47] G. Van Houdt, C. Mosquera, and G. Nápoles, “A review on the long short-term memory model,” Artificial Intelligence Review, vol. 53, no. 8, pp. 5929–5955, Dec. 2020, https://doi.org/10.1007/s10462-020-09838-1
[48] M. Taghi Sattari, K. Yurekli, and M. Pal, “Performance evaluation of artificial neural network approaches in forecasting reservoir inflow,” Applied Mathematical Modelling, vol. 36, no. 6, pp. 2649–2657, Jun. 2012, https://doi.org/10.1016/j.apm.2011.09.048
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Eurasian J. Sci. Eng is distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY-4.0) https://creativecommons.org/licenses/by/4.0/
