Abstract
Among all the natural disasters, drought has the most catastrophic encroachment on the surrounding and environment. Gulbarga, one of the semi-arid districts of Karnataka state, India receives about 700 mm of average annual rainfall and is drought inclined. In this study, the forecasting of drought for the district has been carried out for a lead time of 1 month and 6 months. The multi-temporal Standardized Precipitation Index (SPI) has been used as the drought quantifying parameter due to the fact that it is calculated on the basis of one simplest parameter, i.e., rainfall and additionally due to its ease of use. The fine resolution daily gridded precipitation data (0.25º × 0.25º) procured from Indian Meteorological Department (IMD) of 21 grid locations within the study area have been used for the analysis. Forecasting of drought plays a significant role in drought preparedness and mitigation plans. With the advent of machine learning (ML) techniques over the past few decades, forecasting of any hydrologic event has become easier and more accurate. However, the use of these techniques for drought forecasting is still obscure. In this study, Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques have been employed to examine their accuracy in drought forecasting over shorter and longer lead times. Furthermore, two hybrid approaches have been formulated by coupling a data transformation method with each of the aforementioned ML approaches. At the outset, pre-processing of input data (i.e., SPI) has been carried out using Wavelet Packet Transform (WPT) and then used as inputs to ANN and SVR models to induce hybrid WP-ANN and WP-SVR models. The performance of the hybrid models has been evaluated based on the statistical indices such as R2 (co-efficient of determination), RMSE (Root Mean Square Error), and MAE (Mean Absolute Error). The results showed that the hybrid techniques have better forecast performance than the standalone machine learning approaches. Hybrid WP-ANN model performed relatively better than WP-SVR model for most of the grid locations. Also, the forecasting results deteriorated as the lead time increased from 1 to 6 months.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alley WM (1984) The palmer drought severity index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109. https://doi.org/10.1175/1520-0450(1984)023%3c1100:TPDSIL%3e2.0.CO;2
Bacanli UG, Firat M, Dikbas F (2009) Adaptive neuro-fuzzy inference system for drought forecasting. Stoch Environ Res Risk Assess 23:1143–1154. https://doi.org/10.1007/s00477-008-0288-5
Barua S, Ng AWM, Perera BJC (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
Bazrafshan O, Salajegheh A, Bazrafshan J et al (2015) Hydrological drought forecasting using ARIMA models (case study: Karkheh basin). Ecopersia 3:1099–1117
Belayneh A, Adamowski J (2012) Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Appl Comput Intell Soft Comput 2012:1–13. https://doi.org/10.1155/2012/794061
Belayneh A, Adamowski J, Khalil B (2016) Short-term SPI drought forecasting in the awash river basin in ethiopia using wavelet transforms and machine learning methods. Sustain Water Resour Manag 2:87–101. https://doi.org/10.1007/s40899-015-0040-5
Belayneh A, Adamowski J, Khalil B, Ozga-Zielinski B (2014) Long-term SPI drought forecasting in the awash river basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J Hydrol 508:418–429. https://doi.org/10.1016/j.jhydrol.2013.10.052
Bhalme HN, Mooley DA (1980) Large-scale droughts/floods and monsoon circulation. Mon Weather Rev 108:1197–1211. https://doi.org/10.1175/1520-0493(1980)108%3c1197:LSDAMC%3e2.0.CO;2
Byun H-R, Wilhite DA (1999) Objective quantification of drought severity and duration. J Clim 12:2747–2756. https://doi.org/10.1175/1520-0442(1999)012%3c2747:OQODSA%3e2.0.CO;2
CGWB (2008) Ground Water Information Booklet, Gulbarga District Karnataka. Central Ground Water Board, Bangalore
Chang C-C, Lin C-J (2011) LIBSVM. ACM Trans Intell Syst Technol 2:1–27. https://doi.org/10.1145/1961189.1961199
Chen J, Li M, Wang W (2012) Statistical uncertainty estimation using random forests and its application to drought forecast. Math Probl Eng 2012:1–12. https://doi.org/10.1155/2012/915053
Coifman RR, Meyer Y, Wickerhauser MV (1992) Wavelet analysis and signal processing. In: Ruskai MB (ed) Wavelets and Their Applications. Jones & Bartlett, Boston, pp 153–178
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018
Danandeh Mehr A, Kahya E, Özger M (2014) A gene–wavelet model for long lead time drought forecasting. J Hydrol 517:691–699. https://doi.org/10.1016/j.jhydrol.2014.06.012
Daubechies I (1992) Ten Lectures on Wavelets. Soc Indust Appl Mathemat. https://doi.org/10.1137/1.9781611970104
Ding Y, Hayes MJ, Widhalm M (2011) Measuring economic impacts of drought: a review and discussion. Disaster Prev Manag An Int J 20:434–446. https://doi.org/10.1108/09653561111161752
Djerbouai S, Souag-Gamane D (2016) Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the algerois basin in North Algeria. Water Resour Manag 30:2445–2464. https://doi.org/10.1007/s11269-016-1298-6
Edwards DC, McKee TB (1997) Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report No. 97–2, Colorado State University, Fort Collins
Eslamian S, Dalezios NR, Singh VP, et al (2017) Drought Management. In: Handbook of Drought and Water Scarcity. CRC Press, pp 729–763
Fung KF, Huang YF, Koo CH, Soh YW (2019) Drought forecasting: A review of modelling approaches 2007–2017. J Water Clim Chang. https://doi.org/10.2166/wcc.2019.236
Guttman NB (1998) Comparing the palmer drought index and the standardized precipitation index. J Am Water Resour Assoc 34:113–121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x
Han P, Wang P, Tian M et al (2013) Application of the ARIMA Models in Drought Forecasting Using the Standardized Precipitation Index. In: Li D, Chen Y (eds) Computer and Computing Technologies in Agriculture VI. Springer, Berlin, pp 352–358
Jalalkamali A, Moradi M, Moradi N (2015) Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index. Int J Environ Sci Technol 12:1201–1210. https://doi.org/10.1007/s13762-014-0717-6
Jha MK (2010) Natural and Anthropogenic Disasters: An Overview. Natural and Anthropogenic Disasters. Springer, Netherlands, pp 1–16
Huber PJ, Ronchetti EM (2009) Robust Statistics. John Wiley & Sons, New York. https://doi.org/10.1002/9780470434697
Kallis G (2008) Droughts. Annu Rev Environ Resour 33:85–118. https://doi.org/10.1146/annurev.environ.33.081307.123117
Karavitis CA, Vasilakou CG, Tsesmelis DE et al (2015) Short-term drought forecasting combining stochastic and geo-statistical approaches. Eur Water 49:43–63
Khan GM (2018) Artificial Neural Networks (ANNs). Evolution of Artificial Neural Development. Studies in Computational Intelligence. Springer, Cham, pp 39–55
Kim T-W, Valdés JB (2003) Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. J Hydrol Eng 8:319–328. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(319)
Kohonen T (1988) An introduction to neural computing. Neural Networks 1:3–16. https://doi.org/10.1016/0893-6080(88)90020-2
McKee TB, Nolan J, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Eighth Conference on Applied Climatology. American Meteorological Society, Anaheim
Mehrotra K, Mohan C, Ranka S (1996) Elements of Artificial Neural Networks. The MIT Press, Cambridge
Meyer SJ, Hubbard KG, Wilhite DA (1993) A Crop-Specific Drought Index for Corn: I Model Development Validation. Agron J 85:388. https://doi.org/10.2134/agronj1993.00021962008500020040x
Mishra AK, Desai VR (2005) Drought forecasting using stochastic models. Stoch Environ Res Risk Assess 19:326–339. https://doi.org/10.1007/s00477-005-0238-4
Mishra AK, Singh VP (2011) Drought modeling - A review. J Hydrol 403:157–175. https://doi.org/10.1016/j.jhydrol.2011.03.049
Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111. https://doi.org/10.1002/joc.1498
Nelson D, Wang J (1992) Introduction to artificial neural systems. Neurocomputing 4:328–330. https://doi.org/10.1016/0925-2312(92)90018-K
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644. https://doi.org/10.5194/hess-11-1633-2007
Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: A review. Appl Soft Comput 19:372–386. https://doi.org/10.1016/j.asoc.2014.02.002
Samra JS (2004) Review and analysis of drought monitoring, declaration and management in India. International Water Management Institute, Colombo
Seo Y, Kim S, Kisi O et al (2016) River stage forecasting using wavelet packet decomposition and machine learning models. Water Resour Manag 30:4011–4035. https://doi.org/10.1007/s11269-016-1409-4
Shinde A, Hou Z (2005) A wavelet packet based sifting process and its application for structural health monitoring. Struct Heal Monit An Int J 4:153–170. https://doi.org/10.1177/1475921705049762
Shirmohammadi B, Moradi H, Moosavi V et al (2013) Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Nat Hazards 69:389–402. https://doi.org/10.1007/s11069-013-0716-9
Sivanandam S, Paulraj M (2009) Introduction to Artificial Neural Networks. Vikas Publishing House, New Delhi
Sujay Raghavendra N, Deka PC (2015) Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet–Support vector regression. Cogent Eng 2:999414. https://doi.org/10.1080/23311916.2014.999414
Thornthwaite CW (1948) An Approach toward a Rational Classification of Climate. Geogr Rev 38:55. https://doi.org/10.2307/210739
Titterington M (2010) Neural networks. Wiley Interdiscip Rev Comput Stat 2:1–8. https://doi.org/10.1002/wics.50
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–99. https://doi.org/10.1109/72.788640
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718. https://doi.org/10.1175/2009JCLI2909.1
Walczak B, van den Bogaert B, Massart DL (1996) Application of wavelet packet transform in pattern recognition of near-IR data. Anal Chem 68:1742–1747. https://doi.org/10.1021/ac951091z
Walden AT (2001) Wavelet Analysis of Discrete Time Series. European Congress of Mathematics. Birkhäuser Basel, Basel, pp 627–641
Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1:67–71
WMO (2012) Standardized Precipitation Index: User Guide. World Meteorological Organization, Switzerland
Zargar A, Sadiq R, Naser B, Khan FI (2011) A review of drought indices. Environ Rev 19:333–349. https://doi.org/10.1139/a11-013
Acknowledgements
The authors would like to thank the Indian Meteorological Department, Pune, for providing the necessary data required for research and the Department of Applied Mechanics & Hydraulics, National Institute of Technology Karnataka for the necessary infrastructural support.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Das, P., Naganna, S.R., Deka, P.C. et al. Hybrid wavelet packet machine learning approaches for drought modeling. Environ Earth Sci 79, 221 (2020). https://doi.org/10.1007/s12665-020-08971-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-020-08971-y