ARCHIVES

1. Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021). Predicting at-risk students at
different percentages of course length for early intervention using machine learning models. IEEE Access, 9, 7519–7539.
https://doi.org/10.1109/ACCESS.2021.30494462. AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C. A., Madadgar, S., Papalexiou, S. M., Davis, S. J.,
Hsu, K., & Sorooshian, S. (2022). Status and prospects for drought forecasting: Opportunities in artificial intelligence and hybrid physical–
statistical forecasting. Philosophical Transactions of the Royal Society A, 380(2238), 20210288. https://doi.org/10.1098/rsta.2021.0288
3. Al Mamun, M. A., Sarker, M. R., Sarkar, M. A. R., Roy, S. K., Nihad, S. A. I., McKenzie, A. M., Hossain, M. I., & Kabir, M. S. (2024).
Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm. Scientific
Reports, 14(1), 566. https://doi.org/10.1038/s41598-023-51111-2
4. Alamgir, M., Khan, N., Shahid, S., Yaseen, Z. M., Dewan, A., Hassan, Q., & Rasheed, B. (2020). Evaluating severity–area–frequency of
seasonal droughts in Bangladesh under climate change scenarios. Stochastic Environmental Research and Risk Assessment, 34(2), 447–464.
https://doi.org/10.1007/s00477-020-01768-2
5. Basak, A., Rahman, A. T. M. S., Das, J., Hosono, T., & Kisi, O. (2022). Drought forecasting using the Prophet model in a semi-arid climate
region of western India. Hydrological Sciences Journal, 67(9), 1397–1417. https://doi.org/10.1080/02626667.2022.2082876
6. Bosire, E., Gitau, W., Karanja, F., & Ouma, G. (2019). Analysis of climate variability, trends and expected implications on crop production
in a semi-arid environment of Machakos County, Kenya. International Journal of Scientific Research and Innovative Technology, 6(4), 30–
39.
7. Chien, H.-Y. S., Turek, J. S., Beckage, B., & Eppstein, M. J. (2021). Modeling drought conditions using deep learning techniques.
Environmental Modelling & Software, 144, 105132. https://doi.org/10.1016/j.envsoft.2021.105132
8. Dikshit, A., Pradhan, B., & Alamri, A. M. (2020). Short-term spatio-temporal drought forecasting using random forests model. Applied
Sciences, 10(13), 4516. https://doi.org/10.3390/app10134516
9. Dikshit, A., Pradhan, B., Shukla, A. K., Alamri, A. M., & Alam, S. (2021). Improved drought forecasting using LSTM deep learning model.
Journal of Hydrology, 597, 126190. https://doi.org/10.1016/j.jhydrol.2021.126190
10. Gebrechorkos, S. H., Hülsmann, S., & Bernhofer, C. (2019). Changes in temperature and precipitation extremes in Ethiopia, Kenya, and
Tanzania. International Journal of Climatology, 39(1), 18–30. https://doi.org/10.1002/joc.5777
11. Hu, Y., & Zheng, H. (2019). Time series drought forecasting using LSTM networks. Advances in Meteorology, 2019, 1–10.
https://doi.org/10.1155/2019/3527341
12. Kimaro, J., Massawe, B., & Kimaro, E. (2022). Effects of deforestation and poor agricultural practices on soil moisture content in East Africa.
Environmental Sustainability, 5(1), 38–48.
13. Mahmoud, Y. M., & Mohammed, N. B. (2024). Deep learning for long-range climate forecasting: A comparative study of LSTM and GRU.
Journal of Climate Analytics, 12(1), 55–67.
14. Menculini, L., Pizzi, S., Mancini, M., & Vantaggiato, S. (2021). Comparing Prophet and deep learning to ARIMA in forecasting food prices.
Agricultural Economics, 52(3), 331–342. https://doi.org/10.1111/agec.12616
15. Mustafa Abdullah, M., & Mohsin Abdulazeez, A. (2021). Applications of support vector machines in weather and drought forecasting: A
survey. Journal of Soft Computing and Decision Support Systems, 8(1), 1–11.
16. Nandgude, S., Gupta, A., & Nandgude, P. (2023). Evaluation of SARIMA and SARIMAX in drought forecasting: A multivariate analysis.
Water Resources Management, 37(2), 421–437.
17. Probst, P., Wright, M. N., & Boulesteix, A. L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary
Reviews: Data Mining and Knowledge Discovery, 9(3), e1301.
18. Rezaiy, M., & Shabri, A. (2023). Using ARIMA/SARIMA for Afghanistan’s drought forecasting based on SPI. Climate Risk Management,
39, 100510. https://doi.org/10.1016/j.crm.2023.100510
19. Shohan, M. A. I., Hossain, M. S., & Chowdhury, M. T. (2022). Prophet-LSTM hybrid model for electric load forecasting. Smart Grid and
Sustainable Energy, 13(1), 101–112.
20. Spinoni, J., Vogt, J., Naumann, G., Barbosa, P., & Dosio, A. (2019). Will drought events become more frequent and severe in Europe?
International Journal of Climatology, 39(4), 1445–1462. https://doi.org/10.1002/joc.5858
21. Sundararajan, S., Venkatesan, R., & Arunachalam, D. (2021). Machine learning approaches for drought forecasting: A review. Computers
and Electronics in Agriculture, 187, 106285. https://doi.org/10.1016/j.compag.2021.106285
22. Taylor, S. J., & Letham, B. (2021). Forecasting at scale. The American Statistician, 72(1), 37–45.
https://doi.org/10.1080/00031305.2017.1380080
23. Topping, J. C., Smith, L. M., & White, A. B. (2020). Forecasting air quality using the Prophet time series model. Atmospheric Environment,
242, 117823. https://doi.org/10.1016/j.atmosenv.2020.117823
24. Wainwright, C. M., Finney, D. L., & Funk, C. (2021). Understanding East African soil moisture variability using seasonal rainfall and land
cover data. Climate Dynamics, 57, 987–1002. https://doi.org/10.1007/s00382-020-05629-5
25. Zhao, Y., Li, Z., & Zhang, R. (2022). Regional drought prediction with integrated satellite-based data using Prophet. Remote Sensing, 14(12),
2721. https://doi.org/10.3390/rs14122721