Meteorological and satellite-based data for drought prediction using data-driven model
DOI:
https://doi.org/10.54386/jam.v26i4.2734Keywords:
Drought Deciles index, Meteorological Drought, Multilayer Perceptron, HydrologyAbstract
This work presents a data-driven model, the Artificial Neural Network-Multilayer Perceptron Neural Network (ANN-MLP), for use in meteorological drought deciles index (DDI) predictions over various climatic sub-zone. Two types of rainfall data from meteorological weather stations (WSs) and satellite-based estimates of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network) were adopted. This work considered the calculated DDI (DDI original) from WSs to train and develop the proposed algorithm at three sub-zones (ANN-MLP-DDI models). The newly developed model was tested for DDI prediction using PERSIANN, and compared with the calculated DDI original from WSs. The results positively revealed that the ANN-MLP-DDI models showed high performance (Correlation coefficient r= 0.981) for DDI prediction against the DDI original from WSs. It can be concluded that data-driven models are feasible for drought prediction, and this work could help water managers in mitigating drought impacts and in providing information for policy makers
References
Achite M., Jehanzaib M., Elshaboury N., and Kim T.W (2022). Evaluation of machine learning techniques for hydrological drought modeling: a case study of the Wadi Ouahrane Basin in Algeria. Water 14: 431. https://doi.org/10.3390/w14030431
Ashouri H, Nguyen P, Thorstensen A, Hsu KL, Sorooshian S, and Braithwaite D (2016). Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow. J. Hydrometeorol., 17:2061 – 2076. https://doi.org/10.1175/jhm-d-15-0192.1
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D.K., Knapp, K. R., Cecil, L. D., Nelson, B. R., and Prat, O. P. (2014). PERSIANN-CDR: daily precipitation climate data record from multi-satellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc., 96(1): 69-84. https://doi.org/10.1175/BAMS-D-13-00068.1
Awchi T.A. and Suliman A.H.A. Spatiotemporal assessment of meteorological drought using satellite-based precipitation data over Iraq (2021). IOP Conf. Ser.: Earth Environ. Sci., 779: 012052. https://doi.10.1088/1755-1315/779/1/012052
Barua, S., Ng, A.W.M., and Perera, B.J.C (2012). Artificial neural network–based drought forecasting using a nonlinear aggregated drought index. J. Hydrol. Eng., 17: 1408–1413. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000574
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A. (2017). MSWEP: 3‐hourly 0.25 global gridded precipitation (1979 – 2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21(1): 589 – 615. https://doi.org/10.5194/hess‐21‐589‐2017
Bouaziz M., Medhioub E., and Csaplovisc E (2021). A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions. J. Arid. Environ., 189: 104478. https://doi.org/10.1016/j.jaridenv.2021.104478
Das, P., Zhang, Z., Ghosh, and Hang R. (2024). A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin. Sci Rep., 14: 13870. https://doi.org/10.1038/s41598-024-61520-6
Deo, R.C., Kisi, O., and Singh, V.P. (2017). Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos. Res., 184: 149–175. https://doi.org/10.1016/j.atmosres.2016.10.004
Dikici M (2020). Drought analysis with different indices for the Asi Basin (Turkey). Sci. Rep., 10: 20739. https://doi.org/10.1038/s41598-020-77827-z
Dikshit, A., Pradhan, B., and Alamri, A.M. (2020). Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia. Appl. Sci., 10: 4254. https://doi.org/10.3390/app10124254
Ditthakit P, Pinthong S, Salaeh N, Weekaew J, Tran TT, and Pham QB (2023). Comparative study of machine learning methods and GR2M model for monthly runoff prediction. Ain Shams Eng. J., 14(4):101941. https://doi.org/10.1016/j.asej.2022.101941
Gibbs WJ, and Maher JV (1967). Rainfall Deciles as Drought Indicators. Bureau of Meteorology Bull. 48. Commonwealth of Australia, Melbourne, Australia. doi10.1016/S0022-1694(00)00340-1.
Guo, R.F., and Liu, Y.B. (2016). Evaluation of Satellite Precipitation Products with Rain Gauge Data at Different Scales: Implications for Hydrological Applications. Water, 8: 281. https://doi.org/10.3390/w8070281
Heim, R.R., Jr. (2002). A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc., 83: 1149–1166. https://doi.org/10.3390/w8070281
Ji X., Li Y., Luo X., He D., Guo R., Wang J., Bai Y., Yue C., and Liu C. (2020). Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin. Atmos. Res., 242: 104964. https://doi.org/10.1016/j.atmosres.2020.104964.
Khajehei, S., Ahmadalipour, A., and Moradkhani, H. (2018). An Effective Post ‐ Processing of the North American Multi‐Model Ensemble (NMME) Precipitation Forecasts over the Continental US. Clim. Dyn., 51: 457–472. https://doi.org/10.1007/s00382‐017‐3934‐0
Khan, U., Khalil, A., and Jan, S. (2024). Drought assessment in Kabul River basin using machine learnings. J. Agrometeorol., 26(3): 349–355. https://doi.org/10.54386/jam.v26i3.2674
Le, M.H., Perez, G.C., Solomatine, D., and Nguyen, L.B (2016). Meteorological drought forecasting based on climate signals using Artificial Neural Network—A case study in Khanhhoa Province Vietnam. Procedia Eng., 154: 1169–1175. https://doi.org/10.1016/j.proeng.2016.07.528
McKee T.B., Doeskin N.J., and Kleist J (1993). The relationship of drought frequency and duration to time scales. In: Proceedings of the. In: 8th conference on applied climatology. Anaheim, CA, Jan. 17–23, 1993. Am. Meteorol. Soc., Boston, pp179–184.
Nandgude, N., Singh, T.P., Nandgude, S., and Tiwari, M. (2023). Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies. Sustainability, 2023, 15: 11684. https://doi.org/10.3390/su151511684
Nash JE, and Sutcliffe JV (1970). River flow Forecasting Through Conceptual Models Part I—a Discussion of Principles. J Hydrol., 10(3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Sridhara, S., Chaithra, G.M., and Gopakkali, P. (2021). Assessment and monitoring of drought in Chitradurga district of Karnataka using different drought indices. J. Agrometeorol., 23(2): 221–227. https://doi.org/10.54386/jam.v23i2.72
Suliman A. and Darus I.Z.M. (2019). Semi-distributed neural network models for streamflow prediction in a small catchment Pinang. Environ. Eng. Manage. J., 18(2):535–544. https://eemj.eu/index.php/EEMJ/article/view/3816
Suliman A.H.A., Darus I.Z.M, and Katimon A (2017). GIS-based rainfall-runoff neuro model for streamflow prediction. Int. J. Civ. Eng., 8(5): 235–240. https://doi.org/10.15866/irece.v8i5.12104
Suliman A.H.A., Katimon A. and Darus I.Z.M. (2020). Daily discharge simulation: combining semi-distributed GIS-based and artificial intelligence models. Intern. J. Hydrol. Sci. Technol., 10(5):471–486.
Suliman AHA, Awchi T, and Shahid S (2024) Drought deciles index for spatial and temporal assessment of satellite-based precipitation datasets. Phys Chem Earth A/B/C/,135:103624. https://doi.org/10.1016/j.pce.2024.103624
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L. (2018). A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79 – 107. https://doi.org/10.1002/2017RG000574
Tigkas D, Vangelis H, Proutsos N, and Tsakiris G. (2022). Incorporating aSPI and eRDI in drought indices calculator (DrinC) software for agricultural drought characterization and monitoring. Hydrology 9(6):100. https://doi.org/10.3390/hydrology9060100
Wilhite D. A. and Glantz M. H. (1985). Understanding: The drought phenomenon: The role of definitions. Water Int., 10(3), 111 – 120. https://doi.org/10.1080/02508068508686328
Xia L., Zhao F., Mao K., Yuan Z., Zuo Z., and Xu T (2018). SPI-based analyses of drought changes over the past 60 years in China's major crop-growing areas. Remote Sens., 10 (2), 171. https://doi.org/10.3390/rs10020171
Xu, L., Chen, N., Zhang, X., and Chen, Z. (2018). An evaluation of statistical, NMME and hybrid models for drought prediction in China. J. Hydrol., 566, 235 – 249. https://doi.org/10.1016/j.jhydrol.2018.09.020
Zhong R, Chen X, Lai C, Wang Z, Lian Y, Yu H, and Wu X (2019). Drought monitoring utility of satellite-based precipitation products across mainland China. J Hydrol., 568:343–359. https://doi.org/10.3390/rs11121483
Zhu, Q., Luo, Y., Zhou, D., Xu, Y.-P., Wang, G., and Tian, Y. (2020). Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China. Nat. Hazards,105: 2161–2185. https://doi.org/10.1007/s11069-020-04394-x
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