Abstract
Climate change has increased drought frequency globally, which harms the environment, agriculture, and water resources. This study explores the capacity of a hybrid model based on the integration of extreme learning machine (ELM) with a novel meta-heuristic algorithm called Beluga Whale Optimization (BWO) for drought forecasting within tropical region. The forecasting adopted for the standardized streamflow index (SSI) for several time scales (SSI-1, SSI-3, SS1-6, SSI-9, SSI-12, and SSI-24) over Selangor state, Malaysia. The drought calculation was based on 58 years of stream flow data (1961 to 2018) obtained from two hydrological stations, S3615412 and S3414421. The ELM-BWO model was validated with standalone models including Gradient boosting regression (GBR) and classical ELM in addition to the hybridization of ELM with genetic algorithm (GA). The inputs were nominated by using partial autocorrelation function (PACF) at 5% significance level. The research finding reveals that the ELM-BWO model used for the first in hydrological drought forecasting, outperforms other models and provides the narrowest uncertainty bounds. The proposed ELM-BWO model accuracy was demonstrated by the decreased metric of root mean square error (RMSE) values across different drought timescales (e.g., SSI-9 by 12.65% to 78.74%). In addition, the model obtains the highest correlation coefficient (0.9777 and 0.9944) and Wilmot index values (0.9882 and 0.9971) for both stations. The ELM-BWO model can be used to form a dependable expert intelligent system for anticipating hydrological drought at multiple time scales, making decisions about appropriate measures to deal with hydrological drought at the studied stations, and supporting sustainable water resource management.
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Data are available upon request from the corresponding author.
References
Abd Alraheem E, Jaber NA, Jamei M, Tangang F (2022) Assessment of future meteorological drought under representative concentration pathways (RCP8. 5) scenario: case study of Iraq. Knowledge-Based Eng Sci 3:64–82
Abed M, Imteaz MA, Ahmed AN, Huang YF (2022) A novel application of transformer neural network (TNN) for estimating pan evaporation rate. Appl Water Sci 13:31. https://doi.org/10.1007/s13201-022-01834-w
Achite M, Banadkooki FB, Ehteram M et al (2022a) Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-021-02150-6
Achite M, Bazrafshan O, Azhdari Z, et al (2022b) Forecasting of SPI and SRI Using Multiplicative ARIMA under Climate Variability in a Mediterranean Region: Wadi Ouahrane Basin, Algeria. Clim. 10
Aghelpour P, Varshavian V (2021) Forecasting different types of droughts simultaneously using multivariate standardized precipitation index (MSPI), MLP neural network, and imperialistic competitive algorithm (ICA). Complexity 2021:6610228. https://doi.org/10.1155/2021/6610228
Aghelpour P, Bahrami-Pichaghchi H, Kisi O (2020) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comput Electron Agric 170:105279. https://doi.org/10.1016/j.compag.2020.105279
Aghelpour P, Mohammadi B, Mehdizadeh S et al (2021) A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-021-02011-2
Ahmadianfar I, Jamei M, Karbasi M et al (2021) A novel boosting ensemble committee-based model for local scour depth around non-uniformly spaced pile groups. Eng Comput 38:3439–3461
Alnahit AO, Mishra AK, Khan AA (2022) Stream water quality prediction using boosted regression tree and random forest models. Stoch Environ Res Risk Assess 36(9):2661–2680. https://doi.org/10.1007/s00477-021-02152-4
AlOmar MK, Hameed MM, AlSaadi MA (2020) Multi hours ahead prediction of surface ozone gas concentration: robust artificial intelligence approach. Atmos Pollut Res. https://doi.org/10.1016/j.apr.2020.06.024
AlOmar MK, Khaleel F, AlSaadi AA et al (2022) The influence of data length on the performance of artificial intelligence models in predicting air pollution. Adv Meteorol 2022:5346647. https://doi.org/10.1155/2022/5346647
Alomar MK, Khaleel F, Aljumaily MM et al (2022) Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS ONE 17:e0277079
Azimi H, Bonakdari H, Ebtehaj I (2021) Gene expression programming-based approach for predicting the roller length of a hydraulic jump on a rough bed. ISH J Hydraul Eng 27:77–87. https://doi.org/10.1080/09715010.2019.1579058
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
Banadkooki FB, Singh VP, Ehteram M (2021) Multi-timescale drought prediction using new hybrid artificial neural network models. Nat Hazards 106:2461–2478
Bandyopadhyay J, Rahaman SKH, Karan C (2023) Agricultural potential zone mapping with surface water resource management using geo-spatial tools for Jhargram district, West Bengal, India. Knowledge-Based Eng Sci 4:1–18
Basilio SA, Goliatt L (2022) Gradient boosting hybridized with exponential natural evolution strategies for estimating the strength of geopolymer self-compacting concrete. Knowledge-Based Eng Sci 3:1–16
Bazrafshan J, Hejabi S, Rahimi J (2014) Drought monitoring using the multivariate standardized precipitation index (MSPI). Water Resour Manag 28:1045–1060. https://doi.org/10.1007/s11269-014-0533-2
Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169. https://doi.org/10.1016/j.ijrmms.2013.08.004
Bennett DA (2001) How can I deal with missing data in my study? Aust N Z J Public Health 25:464–469. https://doi.org/10.1111/j.1467-842X.2001.tb00294.x
Cavus Y, Aksoy H (2020) Critical drought severity/intensity-duration-frequency curves based on precipitation deficit. J Hydrol 584:124312. https://doi.org/10.1016/j.jhydrol.2019.124312
Chong KL, Huang YF, Koo CH et al (2022) Spatiotemporal variability analysis of standardized precipitation indexed droughts using wavelet transform. J Hydrol 605:127299. https://doi.org/10.1016/j.jhydrol.2021.127299
Danandeh Mehr A, Rikhtehgar Ghiasi A, Yaseen ZM et al (2022) A novel intelligent deep learning predictive model for meteorological drought forecasting. J Ambient Intell Humaniz Comput 14(8):10441–10455
Deo RC, Şahin M (2015) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos Res 153:512–525. https://doi.org/10.1016/j.atmosres.2014.10.016
Ehteram M, Ahmed AN, Ling L et al (2020) Pipeline scour rates prediction-based model utilizing a multilayer perceptron-colliding body algorithm. Water 12:902
Fan J, Wu L, Zhang F et al (2019) Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: a review and case study in China. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2018.10.018
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
Fung KF, Huang YF, Koo CH, Mirzaei M (2020) Improved svr machine learning models for agricultural drought prediction at downstream of langat river basin. J Water Clim Chang, Malaysia. https://doi.org/10.2166/wcc.2019.295
Gholizadeh R, Yılmaz H, Danandeh Mehr A (2022) Multitemporal meteorological drought forecasting using Bat-ELM. Acta Geophys. https://doi.org/10.1007/s11600-022-00739-1
Goliatt L, Mohammad RS, Abba SI, Yaseen ZM (2023) Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring. Fuel 350:128623
Hadi SJ, Abba SI, Sammen SSH et al (2019) Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation. IEEE Access 7:141533–141548
Hameed MM, Alomar MK, Mohd Razali SF et al (2021) Application of artificial intelligence models for evapotranspiration prediction along the southern coast of Turkey. Complexity. https://doi.org/10.1155/2021/8850243
Hameed MM, Abed MA, Al-Ansari N, Alomar MK (2022a) Predicting compressive strength of concrete containing industrial waste materials: novel and hybrid machine learning model. Adv Civ Eng 2022:5586737. https://doi.org/10.1155/2022/5586737
Hameed MM, AlOmar MK, Al-Saadi AAA, AlSaadi MA (2022b) Inflow forecasting using regularized extreme learning machine: Haditha reservoir chosen as case study. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-022-02254-7
Hameed MM, Khaleel F, AlOmar MK et al (2022c) Optimising the selection of input variables to increase the predicting accuracy of shear strength for deep beams. Complexity 2022:6532763. https://doi.org/10.1155/2022/6532763
Hamzah FB, MohdHamzah F, Mohd Razali SF et al (2020) Imputation methods for recovering streamflow observation: a methodological review. Cogent Environ Sci. https://doi.org/10.1080/23311843.2020.1745133
Hamzah FB, Hamzah FM, Razali SFM, Samad H (2021) A comparison of multiple imputation methods for recovering missing data in hydrological studies. Civ Eng J 7:1608–1619
Hamzah FB, Mohamad Hamzah F, Mohd Razali SF, El-Shafie A (2022) Multiple imputations by chained equations for recovering missing daily streamflow observations: a case study of Langat River basin in Malaysia. Hydrol Sci J 67:137–149. https://doi.org/10.1080/02626667.2021.2001471
Hasan HH, Mohd Razali SF, Muhammad NS, Ahmad A (2019) Research trends of hydrological drought: a systematic review. Water 11(11):2252
Hasan HH, Mohd Razali SF, Muhammad NS, Ahmad A (2021) Hydrological drought across peninsular Malaysia: implication of drought index. Nat Hazards Earth Syst Sci 2021:1–34. https://doi.org/10.5194/nhess-2021-176
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge
Horng SC, Lin SS (2023) Improved Beluga whale optimization for solving the simulation optimization problems with stochastic constraints. Mathematics 11(8):1854
Houssein EH, Sayed A (2023) Dynamic candidate solution boosted beluga whale optimization algorithm for biomedical classification. Mathematics 11:707
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Ibrahim KSMH, Huang YF, Ahmed AN et al (2022) A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alexandria Eng J 61:279–303. https://doi.org/10.1016/j.aej.2021.04.100
Jamei M, Elbeltagi A, Maroufpoor S et al (2022) Combined terrestrial evapotranspiration index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection. Comput Electron Agric 193:106687. https://doi.org/10.1016/j.compag.2022.106687
Kadhum SA, Ishak MY, Zulkifli SZ (2016) Evaluation and assessment of baseline metal contamination in surface sediments from the Bernam River, Malaysia. Environ Sci Pollut Res 23:6312–6321. https://doi.org/10.1007/s11356-015-5853-0
Karbasi M, Jamei M, Malik A et al (2023) Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model. Agric Water Manag. https://doi.org/10.1016/j.agwat.2023.108210
Keskin ME, Terzi Ö, Taylan ED, Küçükyaman D (2009) Meteorological drought analysis using data-driven models for the Lakes District, Turkey. Hydrol Sci J 54:1114–1124. https://doi.org/10.1623/hysj.54.6.1114
Khandelwal M, Armaghani DJ (2016) Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech Geol Eng 34:605–620. https://doi.org/10.1007/s10706-015-9970-9
Kiem AS, Johnson F, Westra S et al (2016) Natural hazards in Australia: droughts. Clim Change 139:37–54. https://doi.org/10.1007/s10584-016-1798-7
Kilinc HC, Ahmadianfar I, Demir V, et al (2023) Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization. Water Resour Manag 1–16
Kisi O, Docheshmeh Gorgij A, Zounemat-Kermani M et al (2019) Drought forecasting using novel heuristic methods in a semi-arid environment. J Hydrol 578:124053. https://doi.org/10.1016/j.jhydrol.2019.124053
Kusin FM, Muhammad SN, Zahar MSM, Madzin Z (2016) Integrated River Basin management: incorporating the use of abandoned mining pool and implication on water quality status. Desalin Water Treat 57:29126–29136. https://doi.org/10.1080/19443994.2016.1168132
Latif SD, Chong KL, Ahmed AN et al (2023) Sediment load prediction in Johor river: deep learning versus machine learning models. Appl Water Sci 13:79. https://doi.org/10.1007/s13201-023-01874-w
Li Z, Chen T, Wu Q et al (2019) Application of penalized linear regression and ensemble methods for drought forecasting in Northeast China. Meteorol Atmos Phys. https://doi.org/10.1007/s00703-019-00675-8
Li Q, Du Y, Liu Z et al (2022) Drought prediction in the Yunnan-Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine. Nat Hazards 113:1635–1661. https://doi.org/10.1007/s11069-022-05361-4
Lipu MSH, Miah MS, Hannan MA, et al (2021) Artificial intelligence based hybrid forecasting approaches for wind power generation: progress, challenges and prospects. IEEE Access 9:102460–102489. https://doi.org/10.1109/ACCESS.2021.3097102
Maghrebi M, Noori R, Bhattarai R et al (2020) Iran’s agriculture in the Anthropocene. Earth’s Futur. https://doi.org/10.1029/2020EF001547
Malik A, Kumar A, Singh RP (2019) Application of heuristic approaches for prediction of hydrological drought using multi-scalar streamflow drought index. Water Resour Manag. https://doi.org/10.1007/s11269-019-02350-4
Malik A, Kumar A, Salih SQ et al (2020) Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India. PLoS ONE. https://doi.org/10.1371/journal.pone.0233280
Malik A, Tikhamarine Y, Sammen SS et al (2021a) Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13445-0
Malik A, Tikhamarine Y, Souag-Gamane D et al (2021b) Support vector regression integrated with novel meta-heuristic algorithms for meteorological drought prediction. Meteorol Atmos Phys 133:891–909. https://doi.org/10.1007/S00703-021-00787-0
Mamat N, Razali SFM (2023) Comparisons of various imputation methods for incomplete water quality data: a case study of The Langat River, Malaysia. J Kejuruter 35:191–201
Mamata RC, Ramlia A, Yazidb MRM, Kasab A, Razalib SFM, Bastamc MN (2022) Slope stability prediction of road embankment using artificial neural network combined with genetic algorithm. J Kejuruter 34(1):165–173
Masinde M (2014) An effective drought early warning system for sub-Saharan Africa: Integrating modern and indigenous approaches. In: Proceedings of the Southern African Institute for Computer Scientist and Information Technologists Annual Conference 2014 on SAICSIT 2014 Empowered by Technology. pp 60–69
Masroor M, Razavi-Termeh SV, Rahaman MH et al (2023) Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India. Stoch Environ Res Risk Assess 37:233–259.
Mckee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. AMS 8th Conf Appl Climatol 179–184 citeulike-article-id:10490403.
Mirboluki A, Mehraein M, Kisi O (2022) Improving accuracy of neuro fuzzy and support vector regression for drought modelling using grey wolf optimization. Hydrol Sci J 67:1582–1597. https://doi.org/10.1080/02626667.2022.2082877
Mishra AK, Desai VR (2006) Drought forecasting using feed-forward recursive neural network. Ecol Modell. https://doi.org/10.1016/j.ecolmodel.2006.04.017
Mishra AK, Singh VP (2009) Analysis of drought severity-area-frequency curves using a general circulation model and scenario uncertainty. J Geophys Res Atmos. https://doi.org/10.1029/2008JD010986
Mishra AK, Desai VR, Singh VP (2007) Drought forecasting using a hybrid stochastic and neural network model. J Hydrol Eng. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:6(626)
Mohamad ET, Faradonbeh RS, Armaghani DJ et al (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28:393–406. https://doi.org/10.1007/s00521-016-2359-8
Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131. https://doi.org/10.1016/j.measurement.2014.08.007
Moreira EE, Martins DS, Pereira LS (2015) Assessing drought cycles in SPI time series using a Fourier analysis. Nat Hazards Earth Syst Sci 15:571–585. https://doi.org/10.5194/nhess-15-571-2015
Mouatadid S, Raj N, Deo RC, Adamowski JF (2018) Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region. Atmos Res 212:130–149. https://doi.org/10.1016/j.atmosres.2018.05.012
Nabipour N, Dehghani M, Mosavi A, Shamshirband S (2020) Short-term hydrological drought forecasting based on different nature-inspired optimization algorithms hybridized with artificial neural networks. IEEE Access 8:15210–15222. https://doi.org/10.1109/ACCESS.2020.2964584
Nguyen V, Li Q, Nguyen L (2017) Drought forecasting using ANFIS- a case study in drought prone area of Vietnam. Paddy Water Environ 15:605–616. https://doi.org/10.1007/s10333-017-0579-x
Nguyen DH, Le XH, Anh DT et al (2022) Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm. J Hydrol 606:127445. https://doi.org/10.1016/j.jhydrol.2022.127445
Obasi GOP (2002) WMO’s role in the international decade for natural disaster reduction. Bull Am Meteorol Soc. https://doi.org/10.1175/1520-0477(1994)075%3c1655:writid%3e2.0.co;2
Otchere DA, Ganat TOA, Ojero JO, et al (2022) Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J Pet Sci Eng 208:109244. https://doi.org/10.1016/j.petrol.2021.109244
Pandey K, Kumar S, Malik A, Kuriqi A (2020) Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh. Sustainability, India, p 12
Park S, Im J, Jang E, Rhee J (2016) Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agric for Meteorol 216:157–169. https://doi.org/10.1016/j.agrformet.2015.10.011
Park H, Kim K, Lee DK (2019) Prediction of severe drought area based on random forest: using satellite image and topography data. Water (switzerland). https://doi.org/10.3390/w11040705
Persson C, Bacher P, Shiga T, Madsen H (2017) Multi-site solar power forecasting using gradient boosted regression trees. Sol Energy 150:423–436. https://doi.org/10.1016/j.solener.2017.04.066
Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol 109:407–414. https://doi.org/10.1007/s00704-012-0592-3
Ridgeway G (2007) Generalized boosted models: a guide to the gbm package. Update 1:2007
Schafer JL (1997) Analysis of incomplete multivariate data. CRC Press
Shamshirband S, Hashemi S, Salimi H et al (2020) Predicting standardized streamflow index for hydrological drought using machine learning models. Eng Appl Comput Fluid Mech. https://doi.org/10.1080/19942060.2020.1715844
Suhaila J, Jemain AA (2012) Spatial analysis of daily rainfall intensity and concentration index in Peninsular Malaysia. Theor Appl Climatol 108:235–245. https://doi.org/10.1007/s00704-011-0529-2
Tabachnick BG, Fidell LS, Ullman JB (2007) Using multivariate statistics. Pearson, Boston, MA
Tan YX, Ng JL, Huang YF (2023) A review on drought index forecasting and their modelling approaches. Arch Comput Methods Eng 30(2):1111–1129
Thom HCS (1958) A note on the gamma distribution. Mon Weather Rev 86:117–122
Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manag. https://doi.org/10.1007/s11269-006-9105-4
Vicente-Serrano SM (2006) Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula. Water Resour Manag 20:37–60. https://doi.org/10.1007/s11269-006-2974-8
Wu J, Chen X, Yu Z et al (2019) Assessing the impact of human regulations on hydrological drought development and recovery based on a ‘simulated-observed’ comparison of the SWAT model. J Hydrol 577:123990. https://doi.org/10.1016/j.jhydrol.2019.123990
Xu D, Zhang Q, Ding Y, Zhang D (2022) Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting. Environ Sci Pollut Res 29:4128–4144. https://doi.org/10.1007/s11356-021-15325-z
Yaman I, Erbay Dalkılıç T (2021) A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Syst Appl 169:114517. https://doi.org/10.1016/j.eswa.2020.114517
Yaseen ZM, Ali M, Sharafati A et al (2021) Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh. Sci Rep. https://doi.org/10.1038/s41598-021-82977-9
Yaseen ZM, Shahid S (2020) Drought Index Prediction Using Data Intelligent Analytic Models: A Review. In: Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Springer, pp 1–27
Zhang Y, Haghani A (2015) A gradient boosting method to improve travel time prediction. Transp Res Part C Emerg Technol 58:308–324. https://doi.org/10.1016/j.trc.2015.02.019
Zhang W, Wang F, Li N (2021) Prediction model of carbon-containing pellet reduction metallization ratio using neural network and genetic algorithm. ISIJ Int 61:1915–1926
Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowled-Based Syst 251:109215. https://doi.org/10.1016/j.knosys.2022.109215
Acknowledgements
The authors would like to thank the Universiti Kebangsaan Malaysia for supporting this research. The authors also want to express their deep gratitude to the Department of Irrigation and Drainage Malaysia for providing the streamflow data. Zaher Mundher Yaseen would like to reveal his appreciation to the Civil and Environmental Engineering department, King Fahd University of Petroleum & Minerals, Saudi Arabia for its admirable support.
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MMH: Conceptualization; data curation; formal analysis; methodology; investigation; visualization; writing—original draft,—review & editing draft preparation; resources; software. SFMR: Supervision, conceptualization; project administration; writing—review & editing. WHMWM: Supervision, conceptualization; project administration; writing—review & editing. ZMY: Supervision, conceptualization; project administration; writing—review & editing.
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Hameed, M.M., Mohd Razali, S.F., Wan Mohtar, W.H.M. et al. Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm. Stoch Environ Res Risk Assess 37, 4963–4989 (2023). https://doi.org/10.1007/s00477-023-02548-4
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DOI: https://doi.org/10.1007/s00477-023-02548-4