{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:02:09Z","timestamp":1775869329062,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Water and Energy Center, United Arab Emirates University","award":["31R281-AUA- NWEC-4-2020"],"award-info":[{"award-number":["31R281-AUA- NWEC-4-2020"]}]},{"name":"National Water and Energy Center, United Arab Emirates University","award":["12R023-AUA- NWEC -4- 2020"],"award-info":[{"award-number":["12R023-AUA- NWEC -4- 2020"]}]},{"name":"National Water and Energy Center, United Arab Emirates University","award":["12R019-NWEC-6-2020"],"award-info":[{"award-number":["12R019-NWEC-6-2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Drought forecasting is essential for risk management and preparedness of drought mitigation measures. The present study aims to evaluate the effectiveness of the proposed hybrid technique for regional drought forecasting. Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and two wavelet techniques, namely, Discrete Wavelet Transform (DWT) and Wavelet Packet Transform (WPT), were evaluated in drought forecasting up to a lead time of six months. Standard error metrics were used to select optimal model parameters, such as number of inputs, number of hidden neurons, level of decomposition, and number of mother wavelets. Additionally, the performance of various mother wavelets, including the Haar wavelet (db1) and 19 Daubechies wavelets (db1 to db20), were evaluated. The results indicated that the ANN model produced better forecasts than the MLR model, whereas the hybrid models outperformed both ANN and MLR models, which failed to predict the SPI values for a lead time greater than two months. The performance of all the models was found to improve as the timescale increased from 3 to 12 months. However, all the models\u2019 performances deteriorated as the lead time increased. The hybrid WPT-MLR was the best model for the study area. The findings indicated that a hybrid WPT-MLR model could be used for drought early warning systems in the study area.<\/jats:p>","DOI":"10.3390\/rs14246381","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T08:41:41Z","timestamp":1671439301000},"page":"6381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Evaluation of Hybrid Wavelet Models for Regional Drought Forecasting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-8482","authenticated-orcid":false,"given":"Gilbert","family":"Hinge","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur 713209, West Bengal, India"}]},{"given":"Jay","family":"Piplodiya","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2613-2821","authenticated-orcid":false,"given":"Ashutosh","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1797-4904","authenticated-orcid":false,"given":"Mohamed A.","family":"Hamouda","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"},{"name":"National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1412-7219","authenticated-orcid":false,"given":"Mohamed M.","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"},{"name":"National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s12665-020-08971-y","article-title":"Hybrid wavelet packet machine learning approaches for drought modeling","volume":"79","author":"Das","year":"2020","journal-title":"Environ. Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chang, S., Chen, H., Wu, B., Nasanbat, E., Yan, N., and Davdai, B. (2021). A practical satellite-derived vegetation drought index for arid and semi-arid grassland drought monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13030414"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1080\/02626667.2021.1934473","article-title":"Critical drought intensity-duration-frequency curves based on total probability theorem-coupled frequency analysis","volume":"66","author":"Aksoy","year":"2021","journal-title":"Hydrol. Sci. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"128097","DOI":"10.1016\/j.jhydrol.2022.128097","article-title":"Modified drought severity index: Model improvement and its application in drought monitoring in China","volume":"612","author":"Sun","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ecolmodel.2006.04.017","article-title":"Drought forecasting using feed-forward recursive neural network","volume":"198","author":"Mishra","year":"2006","journal-title":"Ecol. Modell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105380","DOI":"10.1016\/j.atmosres.2020.105380","article-title":"Monitoring meteorological drought in a semiarid region using two long-term satellite-estimated rainfall datasets: A case study of the Piranhas River basin, northeastern Brazil","volume":"250","author":"Santos","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y.-A., Mushore, T.D., and Gupta, A. (2020). Impacts of drought on vegetation assessed by vegetation indices and meteorological factors in Afghanistan. Remote Sens., 12.","DOI":"10.3390\/rs12152433"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1007\/s11069-016-2520-9","article-title":"A fuzzy c-means approach regionalization for analysis of meteorological drought homogeneous regions in western India","volume":"84","author":"Goyal","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_9","unstructured":"Samra, J.S. (2004). Review and Analysis of Drought Monitoring, Declaration and Management in India, IWMI."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hinge, G., Mohamed, M.M., Long, D., and Hamouda, M.A. (2021). Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward. Remote Sens., 13.","DOI":"10.3390\/rs13214353"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1007\/s00477-020-01949-z","article-title":"Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran","volume":"35","author":"Aghelpour","year":"2021","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1007\/s11069-011-9867-8","article-title":"District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI)","volume":"59","author":"Pai","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hinge, G., and Sharma, A. (2022, January 23\u201327). Comparison of wavelet and machine learning methods for regional drought prediction. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria.","DOI":"10.5194\/egusphere-egu21-218"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1007\/s11069-009-9493-x","article-title":"Application of meteorological and vegetation indices for evaluation of drought impact: A case study for Rajasthan, India","volume":"54","author":"Jain","year":"2010","journal-title":"Nat. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"801","DOI":"10.5194\/nhess-16-801-2016","article-title":"Impacts of European drought events: Insights from an international database of text-based reports","volume":"16","author":"Stahl","year":"2016","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_16","unstructured":"McKee, T.B., Doesken, N.J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Boston, MA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_18","unstructured":"Palmer, W.C. (1965). Meteorological Drought, US Department of Commerce, Weather Bureau."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1175\/JHM-386.1","article-title":"A global dataset of Palmer Drought Severity Index for 1870\u20132002: Relationship with soil moisture and effects of surface warming","volume":"5","author":"Dai","year":"2004","journal-title":"J. Hydrometeorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1002\/joc.1976","article-title":"Use of drought indices in climate change impact assessment studies: An application to Greece","volume":"30","author":"Mavromatis","year":"2010","journal-title":"Int. J. Climatol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1175\/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2","article-title":"A self-calibrating Palmer drought severity index","volume":"17","author":"Wells","year":"2004","journal-title":"J. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1080\/00431672.1968.9932814","article-title":"Keeping track of crop moisture conditions, nationwide: The new crop moisture index","volume":"21","author":"Palmer","year":"1968","journal-title":"Weatherwise"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/j.1752-1688.1998.tb05964.x","article-title":"Comparing the palmer drought index and the standardized precipitation index 1","volume":"34","author":"Guttman","year":"1998","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jhydrol.2011.03.049","article-title":"Drought modeling\u2013A review","volume":"403","author":"Mishra","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s00477-005-0238-4","article-title":"Drought forecasting using stochastic models","volume":"19","author":"Mishra","year":"2005","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_26","first-page":"43","article-title":"Short-term drought forecasting combining stochastic and geo-statistical approaches","volume":"49","author":"Karavitis","year":"2015","journal-title":"Eur. Water"},{"key":"ref_27","first-page":"1099","article-title":"Hydrological drought forecasting using ARIMA models (case study: Karkheh Basin)","volume":"3","author":"Bazrafshan","year":"2015","journal-title":"Ecopersia"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.mcm.2009.10.031","article-title":"Drought forecasting based on the remote sensing data using ARIMA models","volume":"51","author":"Han","year":"2010","journal-title":"Math. Comput. Model."},{"key":"ref_29","unstructured":"Kigumi, J.M. (2018). Use of Earth Observation Data and Artificial Neural Networks for Drought Forecasting: Case Study of Narumoro Sub-Catchment. [Ph.D. Thesis, Pan African University]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1175\/JHM-D-15-0115.1","article-title":"Uncertainty and bias in satellite-based precipitation estimates over Indian subcontinental basins: Implications for real-time streamflow simulation and flood prediction","volume":"17","author":"Shah","year":"2016","journal-title":"J. Hydrometeorol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"405","DOI":"10.5109\/1543403","article-title":"Adaptive Neuro\u2013Fuzzy Inference System for Drought Forecasting in the Cai River Basin in Vietnam","volume":"60","author":"Nguyen","year":"2015","journal-title":"J. Fac. Agric. Kyushu Univ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1002\/joc.1498","article-title":"Drought forecasting using artificial neural networks and time series of drought indices","volume":"27","author":"Morid","year":"2007","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1002\/wcc.81","article-title":"Drought under global warming: A review","volume":"2","author":"Dai","year":"2011","journal-title":"Wiley Interdiscip. Rev. Clim. Chang."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1016\/j.jhydrol.2013.10.052","article-title":"Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models","volume":"508","author":"Belayneh","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s40899-015-0040-5","article-title":"Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods","volume":"2","author":"Belayneh","year":"2016","journal-title":"Sustain. Water Resour. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1061\/(ASCE)1084-0699(2003)8:6(319)","article-title":"Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks","volume":"8","author":"Kim","year":"2003","journal-title":"J. Hydrol. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jhydrol.2016.05.042","article-title":"Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts","volume":"539","author":"Maity","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1007\/s11269-016-1298-6","article-title":"Drought forecasting using neural networks, wavelet neural networks, and stochastic models: Case of the Algerois Basin in North Algeria","volume":"30","author":"Djerbouai","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.jhydrol.2014.06.012","article-title":"A gene\u2013wavelet model for long lead time drought forecasting","volume":"517","author":"Mehr","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1007\/s00477-016-1265-z","article-title":"Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model","volume":"31","author":"Deo","year":"2017","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_41","first-page":"794061","article-title":"Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression","volume":"2012","author":"Belayneh","year":"2012","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"975","DOI":"10.2166\/hydro.2018.115","article-title":"Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)","volume":"20","author":"Komasi","year":"2018","journal-title":"J. Hydroinform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_44","first-page":"12","article-title":"Analysis of rainfall and drought in Rajasthan State, India","volume":"17","author":"Mundetia","year":"2015","journal-title":"Glob. Nest J"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s40808-021-01102-x","article-title":"Analyzing the extent of drought in the Rajasthan state of India using vegetation condition index and standardized precipitation index","volume":"8","author":"Mishra","year":"2022","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_46","first-page":"558","article-title":"A high resolution daily gridded rainfall dataset (1971\u20132005) for mesoscale meteorological studies","volume":"96","author":"Rajeevan","year":"2009","journal-title":"Curr. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Adane, G.B., Hirpa, B.A., Song, C., and Lee, W.-K. (2020). Rainfall characterization and trend analysis of wet spell length across varied landscapes of the Upper Awash River Basin, Ethiopia. Sustainability, 12.","DOI":"10.3390\/su12219221"},{"key":"ref_48","unstructured":"GUIDE, WMO Standardized Precipitation Index User, Svoboda, M., Hayes, M., and Wood, D. (2012). World Meteorological Organization: Geneva, WMO."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","article-title":"Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research","volume":"22","author":"Beresford","year":"2000","journal-title":"J. Pharm. Biomed. Anal."},{"key":"ref_50","first-page":"27","article-title":"An introduction to artificial neural network","volume":"1","author":"Kukreja","year":"2016","journal-title":"Int. J. Adv. Res. Innov. Ideas Educ."},{"key":"ref_51","unstructured":"Brace, M.C., Schmidt, J., and Hadlin, M. (1991, January 18-21). Comparison of the forecasting accuracy of neural networks with other established techniques. Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, Singapore."},{"key":"ref_52","first-page":"13","article-title":"Applications of soft computing in civil engineering: A review","volume":"81","author":"Chandwani","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_53","first-page":"1","article-title":"Multiple linear regression","volume":"5","author":"Tranmer","year":"2008","journal-title":"Cathie Marsh Cent. Census Surv. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1137\/0515056","article-title":"Decomposition of Hardy functions into square integrable wavelets of constant shape","volume":"15","author":"Grossmann","year":"1984","journal-title":"SIAM J. Math. Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1175\/JHM-D-10-05007.1","article-title":"Long lead time drought forecasting using a wavelet and fuzzy logic combination model: A case study in Texas","volume":"13","author":"Mishra","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3583","DOI":"10.1002\/hyp.7461","article-title":"Wavelet regression model as an alternative to neural networks for monthly streamflow forecasting","volume":"23","year":"2009","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.cageo.2011.12.015","article-title":"Comparative study of different wavelets for hydrologic forecasting","volume":"46","author":"Maheswaran","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.atmosres.2016.07.030","article-title":"Analysis of trends and dominant periodicities in drought variables in India: A wavelet transform based approach","volume":"182","author":"Joshi","year":"2016","journal-title":"Atmos. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"125380","DOI":"10.1016\/j.jhydrol.2020.125380","article-title":"Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting","volume":"590","author":"Khan","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1080\/02626667.2020.1754422","article-title":"Assessment of drought trend and variability in India using wavelet transform","volume":"65","author":"Sharma","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.17654\/EC017051065","article-title":"Comparison of discrete wavelet transform and wavelet packet decomposition for the lung sound classification","volume":"17","author":"Rizal","year":"2017","journal-title":"Far East J. Electron. Commun."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jhydrol.2014.03.057","article-title":"Applications of hybrid wavelet\u2013artificial intelligence models in hydrology: A review","volume":"514","author":"Nourani","year":"2014","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6381\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:07Z","timestamp":1760146987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":62,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246381"],"URL":"https:\/\/doi.org\/10.3390\/rs14246381","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}