Machine Learning-Based Assessment of Meteorological Droughts in Chitral and Swat River Basins, Pakistan
Meteorological droughts in Chitral and Swat River Basins
Keywords:
Drought prediction, Climate Change, Machine Learning, Spatio-temporal droughtAbstract
The detrimental effects of droughts on water resources and agriculture can lead to significant economic losses and risk to lives. Using key climatic factors to analyze changes in a relevant index, this study aims to forecast droughts. The study is structured into three distinct phases. First, the computation of the Standardized Precipitation Evapotranspiration Index (SPEI) for the Chitral and Swat River basins was carried out using data from 1981 to 2022. This index is designed to predict both short-term and long-term droughts. Second, the dataset was split into training and testing sets, with 80% designated for training and 20% for testing the models, employing algorithms such as XGBoost, Decision Tree, AdaBoost, and Linear Regression, along with various climate variables. Finally, the models were evaluated using statistical metrics like R² (Coefficient of Determination), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and MSE (Mean Squared Error), and future predictions from 2023 to 2045 were made based on the well-trained and tested models. The results demonstrate promising performance, with R² values of 0.968, 0.906, 0.901, and 0.287, and RMSE values of 0.265, 0.291, 0.302, and 0.837 for XGBoost, AdaBoost, Decision Tree, and Linear Regression, respectively. The SPEI shows potential as a useful tool for drought prediction, and spatial distribution mapping in ArcMap using the Inverse Distance Weighting method reveals persistent moderate droughts in both basins. Additional research using a larger dataset or combining data from different areas could enhance the applicability of the findings and lead to a deeper understanding.
References
Alami, M. M., Hayat, E., & Tayfur, G. (2017). Proposing a Popular Method for Meteorological Drought Monitoring in the Kabul River Basin, Afghanistan. International Journal of Advanced Engineering Research and Science, 4(6), 103–110. https://doi.org/10.22161/ijaers.4.6.12
Ali, A. F., Xiao, C., Zhang, X., Adnan, M., Iqbal, M., & Khan, G. (2018). Projection of future streamflow of the Hunza River Basin, Karakoram Range (Pakistan) using the HBV hydrological model. Journal of Mountain Science, 15(10), 2218–2235. https://doi.org/10.1007/s11629-018-4907-4
Ali, S., Ajmal, M., Khan, M. S., & Shah, S. U. (2015). Assessment of precipitation trends in Gilgit Baltistan (Pakistan) for the period 1980−2015: An indicator of climate change.
Baig, M. H. A., Abid, M., Khan, M. R., Jiao, W., Amin, M., & Adnan, S. (2020). Assessing Meteorological and Agricultural Drought in Chitral Kabul River Basin Using Multiple Drought Indices. Remote Sensing, 12(9), 1417. https://doi.org/10.3390/rs12091417
Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., & Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758
DeChant, C. M., & Moradkhani, H. (2015). Analyzing the sensitivity of drought recovery forecasts to land surface initial conditions. Journal of Hydrology, 526, 89–100. https://doi.org/10.1016/j.jhydrol.2014.10.021
Duffner, S., & Garcia, C. (2007). An Online Backpropagation Algorithm with Validation Error-Based Adaptive Learning Rate (pp. 249–258). https://doi.org/10.1007/978-3-540-74690-4_26
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Hao, Z., Singh, V. P., & Xia, Y. (2018). Seasonal Drought Prediction: Advances, Challenges, and Future Prospects. Reviews of Geophysics, 56(1), 108–141. https://doi.org/10.1002/2016RG000549
Iqbal, M. S., Dahri, Z. H., Querner, E. P., Khan, A., & Hofstra, N. (2018). Impact of climate change on flood frequency and intensity in the Kabul River basin. Geosciences (Switzerland), 8(4). https://doi.org/10.3390/GEOSCIENCES8040114
Jan, S., Khan, U., Khalil, A., Khan, A. A., Jan, H. A., & Ullah, I. (2024). Use of machine learning techniques in predicting inflow in the Tarbela reservoir of the Upper Indus Basin. Journal of Agrometeorology, 26(4), 501–504. https://doi.org/10.54386/jam.v26i4.2676
Khan, M. A., Khattak, M. S., & Khan, A. (2022). Selection of the Most Suitable Gridded Precipitation and Temperature Datasets for the Kabul River Basin based on Statistical Indices—A Transboundary Basin between Pakistan and Afghanistan.
Khan, U., Khalil, A., & Jan, S. (2024). Drought assessment in the Kabul River basin using machine learning. Journal of Agrometeorology, 26(3), 349–355. https://doi.org/10.54386/jam.v26i3.2674
Khattak, M. S., Reman, N. U., Sharif, M., & Khan, A. (2017). Analysis of streamflow data for trend detection on major rivers of the Indus Basin.
Lloyd-Hughes, B. (2014). The impracticality of a universal drought definition. Theoretical and Applied Climatology, 117(3–4), 607–611. https://doi.org/10.1007/S00704-013-1025-7
Marj, A. F., & Meijerink, A. M. J. (2011). Agricultural drought forecasting using satellite images, climate indices, and an artificial neural network. International Journal of Remote Sensing, 32(24), 9707–9719. https://doi.org/10.1080/01431161.2011.575896
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., Van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747–756. https://doi.org/10.1038/nature08823
Nandgude, N., Singh, T. P., Nandgude, S., & Tiwari, M. (2023). Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies. Sustainability, 15(15), 11684. https://doi.org/10.3390/su151511684
Omali, T. U. (2022). Correlation of Geographic Information Systems with the Evolutionary Theory of Spatial Analysis. International Journal of Scientific Research in Computer Science and Engineering, 10 (4), 18-22.
Orimoloye, I. R., Belle, J. A., Orimoloye, Y. M., Olusola, A. O., & Ololade, O. O. (2022). Drought: A Common Environmental Disaster. Atmosphere, 13(1), 111. https://doi.org/10.3390/ATMOS13010111.
Rahman, G., Rahman, A., Ullah, S., Dawood, M., Moazzam, M. F. U., & Lee, B. G. (2021). Spatio-temporal characteristics of meteorological drought in Khyber Pakhtunkhwa, Pakistan. PLOS ONE, 16(4), e0249718. https://doi.org/10.1371/journal.pone.0249718
Raposo, V. de M. B., Costa, V. A. F., & Rodrigues, A. F. (2023). A review of recent developments on drought characterization, propagation, and influential factors. Science of The Total Environment, 898, 165550. https://doi.org/10.1016/j.scitotenv.2023.165550
Rezaiy, R., & Shabri, A. (2023). Drought forecasting using the W-ARIMA model with the standardised precipitation index. Journal of Water and Climate Change, 14(9), 3345–3367. https://doi.org/10.2166/wcc.2023.431
Setiono, R., Wee Kheng Leow, & Zurada, J. M. (2002). Extraction of rules from artificial neural networks for nonlinear regression. IEEE Transactions on Neural Networks, 13(3), 564–577. https://doi.org/10.1109/TNN.2002.1000125
Sidiqi, M., Kasiviswanathan, K. S., Scheytt, T., & Devaraj, S. (2023). Assessment of Meteorological Drought under Climate Change in the Kabul River Basin, Afghanistan. Atmosphere, 14(3), 570. https://doi.org/10.3390/atmos14030570
Syed, Z., Ahmad, S., Dahri, Z. H., Azmat, M., Shoaib, M., Inam, A., Qamar, M. U., Hussain, S. Z., & Ahmad, S. (2022). Hydroclimatology of the Chitral River in the Indus Basin under Changing Climate. Atmosphere, 13(2), 295. https://doi.org/10.3390/atmos13020295
Taraky, Y. M., McBean, E., Liu, Y., Daggupati, P., Shrestha, N. K., Jiang, A., & Gharabaghi, B. (2021). The Role of Large Dams in a Transboundary Drought Management Co-Operation Framework—Case Study of the Kabul River Basin. Water, 13(19), 2628. https://doi.org/10.3390/w13192628
Teutschbein, C., Albrecht, F., Blicharska, M., Tootoonchi, F., Stenfors, E., & Grabs, T. (2023). Drought hazards and stakeholder perception: Unraveling the interlinkages between drought severity, perceived impacts, preparedness, and management. Ambio, 52(7), 1262–1281. https://doi.org/10.1007/S13280-023-01849-W/FIGURES/7
Tramblay, Y., Koutroulis, A., Samaniego, L., Vicente-Serrano, S. M., Volaire, F., Boone, A., Le Page, M., Llasat, M. C., Albergel, C., Burak, S., Cailleret, M., Kalin, K. C., Davi, H., Dupuy, J.-L., Greve, P., Grillakis, M., Hanich, L., Jarlan, L., Martin-StPaul, N., & Polcher, J. (2020). Challenges for drought assessment in the Mediterranean region under future climate scenarios. Earth-Science Reviews, 210, 103348. https://doi.org/10.1016/j.earscirev.2020.103348
Van Vuuren, D. P., Kriegler, E., O’Neill, B. C., Ebi, K. L., Riahi, K., Carter, T. R., Edmonds, J., Hallegatte, S., Kram, T., Mathur, R., & Winkler, H. (2014). A new scenario framework for Climate Change Research: Scenario matrix architecture. Climatic Change, 122(3), 373–386. https://doi.org/10.1007/S10584-013-0906-1/FIGURES/6
Yao, Y., Fu, B., Liu, Y., Li, Y., Wang, S., Zhan, T., Wang, Y., & Gao, D. (2022). Evaluation of ecosystem resilience to drought based on drought intensity and recovery time. Agricultural and Forest Meteorology, 314, 108809. https://doi.org/10.1016/J.AGRFORMET.2022.108809
