1. Home
  2. 2024-V10-I2
  3. Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal
Statistics

Article Views: 242

PDF Downloads: 23

  • Date of Publication : 2024-04-28 Article Type : Research Article
  • Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal

    Supath Dhital*¹’ ², Kapil Lamsal ², Sulav Shrestha ², and  Umesh Bhurtyal ²

    Affiliation

    ¹ Department of Geography and Environment Studies, The University of Alabama, Tuscaloosa AL-USA
    ² Department of Geomatics Engineering, Faculty of Engineering, Tribhuvan University, Pokhara-Nepal
    *Corresponding Author


    ORCID :

    Supath Dhital: https://orcid.org/0000-0002-9535-8544, Kapil Lamsal:  https://orcid.org/0009-0002-7630-9605Sulav Shrestha: https://orcid.org/0009-0001-0203-9262Umesh Bhurtyal:  https://orcid.org/0009-0009-9154-9683


    DOI :

    https://doi.org/10.23918/eajse.v10i2p02


    Article History

    Received: 2023-07-10

    Revised: 2024-03-03

    Accepted: 2024-04-04

    Abstract

    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.

    Keywords :

    Weather Forecast; Deep Learning; Long Short-Term Memory (LSTM); Meteorological Data; Precipitation; Air Temperature


    [1]    R.B. Stull, “An introduction to boundary layer meteorology”. Vol. 13. Springer Science & Business Media, 2012. Accessed: Mar. 28, 2024.

    Google Scholar 
    [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.

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [27]    D. Shah, “Short term temperature forecasting using LSTMS, and CNN,” 2021, Accessed: Mar. 28, 2024. https://digitalcommons.njit.edu/theses/1840

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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.

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [42]     “World weather, climate and accurate forecast information.” Accessed: Mar. 28, 2024. [Online]. Available: https://weather-and-climate.com/

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 
    [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

    Google Scholar 



    @article{dhital,supathandlamsal,kapilandshrestha,sulavandbhurtyal,umesh2024,
     author = {Dhital, Supath and Lamsal, Kapil and Shrestha, Sulav and Bhurtyal, Umesh},
     title = {Forecasting Weather using Deep Learning from the Meteorological  Stations Data : A Study of  Different Meteorological Stations in Kaski District, Nepal},
     journal = {Eurasian J. Sci. Eng},
     volume = {10},
     number = {2},
     pages = {16-33},
     year = {2024}
    }
    Copy

    Dhital, S., Lamsal, K., Shrestha, S., & Bhurtyal, U. (2024). Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal. Eurasian J. Sci. Eng, 10(2),16-33.

    Copy

    Dhital, Supath, et al. "Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal." Eurasian J. Sci. Eng, 10.2, (2024), pp.16-33.

    Copy

    Dhital, S., Lamsal, K., Shrestha, S., & Bhurtyal, U. (2024) "Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal", Eurasian J. Sci. Eng, 10(2), pp.16-33.

    Copy

    Dhital S, Lamsal K, Shrestha S, Bhurtyal U.. Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal. Eurasian J. Sci. Eng. 2024; 10(2):16-33.

    Copy

    Under Development

    Under Development

    Under Development

  • Forecasting Weather using Deep Learning from the Meteorological Stations Data : A Study of Different Meteorological Stations in Kaski District, Nepal