Abstract
Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000–2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows’ (Cp), Akaike’s (AIC), Schwarz’s (SBC), and Amemiya’s PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient (\(r\)), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of \(r\), MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.
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Data availability and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- RF:
-
Random forest
- RT:
-
Random tree
- GPR:
-
Gaussian process regression
- SPI:
-
Standardized precipitation index
- MSE:
-
Mean square error
- RMSE:
-
Root mean square error
- RAE:
-
Relative absolute error
- RRSE:
-
Root relative squared error
- IPCC:
-
Intergovernmental Panel on Climate Change
- EDI:
-
Effective drought index
- SPEI:
-
Standardized precipitation evapotranspiration index
- SPI:
-
Standardized precipitation index
- PDSI:
-
Palmer drought severity index
- PDN:
-
Percent departure from normal
- VCI:
-
Vegetation condition index
- RDI:
-
Reconnaissance drought index
- ML:
-
Machine learning
- DML:
-
Deep machine learning
- ANNs:
-
Artificial neural networks
- ANFIS:
-
Adaptive neuro-fuzzy inference system
- WANN:
-
Wavelet-based artificial neural system
- SVM:
-
Support vector machine
- M5P:
-
M5 pruning tree
- MARS:
-
Multivariate adaptive regression splines
- REPTree:
-
Error pruning tree
- RSS:
-
Random subspace
- MLP:
-
Multilayer perceptron
- GEP:
-
Gene expression programming
- ELM:
-
Extreme learning machine
- ARIMA-ANN:
-
Autoregressive integrated moving average-artificial neural network
- WANFIS:
-
Wavelet-based artificial-fuzzy inference system
- GIS:
-
Geographic information system
- R 2 :
-
Coefficient of determination
References
Achieng KO (2019) Modelling of soil moisture retention curve using machine learning techniques: artificial and deep neural networks vs support vector regression models. Comput Geosci 133:104320. https://doi.org/10.1016/j.cageo.2019.104320
Algur KD, Patel SK, Chauhan S (2021) The impact of drought on the health and livelihoods of women and children in India: a systematic review. Child Youth Serv Rev 122:105909. https://doi.org/10.1016/j.childyouth.2020.105909
Alizamir M, Kisi O, Ahmed AN et al (2020) Advanced machine learning model for better prediction accuracy of soil temperature at different depths. PLoS One 15:e0231055. https://doi.org/10.1371/journal.pone.0231055
Alley WM (1984) The Palmer drought severity index: limitations and assumptions. J Appl Meteorol Climatol 23:1100–1109. https://doi.org/10.1175/1520-0450(1984)023%3c1100:TPDSIL%3e2.0.CO;2
Al-Mukhtar M (2021) Modeling the monthly pan evaporation rates using artificial intelligence methods: a case study in Iraq. Environ Earth Sci 80:39. https://doi.org/10.1007/s12665-020-09337-0
Anandharuban P, Elango L (2021) Spatio-temporal analysis of rainfall, meteorological drought and response from a water supply reservoir in the megacity of Chennai, India. J Earth Syst Sci 130:17. https://doi.org/10.1007/s12040-020-01538-2
Aouani H, Slimani M, Hamrouni S et al (2018) Data concerning the psychometric properties of the “Profile of Emotional Competence” questionnaire administered to a sample of athletes and non-athletes. Data Br 18:769–775. https://doi.org/10.1016/j.dib.2018.03.067
Bachmair S, Stahl K, Collins K et al (2016) Drought indicators revisited: the need for a wider consideration of environment and society. Wires Water 3:516–536. https://doi.org/10.1002/wat2.1154
Barzegar R, AsghariMoghaddam A, Adamowski J, Ozga-Zielinski B (2018) Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stoch Environ Res Risk Assess 32:799–813. https://doi.org/10.1007/s00477-017-1394-z
Bates BC, Kundzewicz ZW, Wu S, Palutik JP (2008b) Climate change and water - IPCC technical paper VI. IPCC Secretariat, Geneva
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
Bhunia P, Das P, Maiti R (2020) Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst Environ 4:43–55. https://doi.org/10.1007/s41748-019-00137-6
Bidkar KL, Jadhao PD (2019) Prediction of strength of remixed concrete by application of orthogonal decomposition, neural analysis and regression analysis. Open Eng 9:434–443. https://doi.org/10.1515/eng-2019-0053
Bouaziz M, Medhioub E, 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
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Chen J, Li M, Wang W (2012) Statistical uncertainty estimation using random forests and its application to drought forecast. Math Probl Eng 2012:915053. https://doi.org/10.1155/2012/915053
Choubin B, Malekian A (2017) Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions. Environ Earth Sci 76:538. https://doi.org/10.1007/s12665-017-6870-8
Cutler DR, Edwards TC Jr, Beard KH et al (2007) Random forests for classification in ecology. Ecology 88:2783–2792. https://doi.org/10.1890/07-0539.1
Dash Y, Mishra SK, Panigrahi BK (2018) Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Comput Electr Eng 70:66–73. https://doi.org/10.1016/j.compeleceng.2018.06.004
Diaz HF (1983) Drought in the United State. J Clim Appl Meteorol 22:3–16. https://doi.org/10.1175/1520-0450(1983)022%3c0003:DITUS%3e2.0.CO;2
Dixit S, Tayyaba S, Jayakumar KV (2021) Spatio-temporal variation and future risk assessment of projected drought events in the Godavari River basin using regional climate models. J Water Clim Chang. https://doi.org/10.2166/wcc.2021.093
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
El Bilali A, Taleb A, Brouziyne Y (2021) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 245:106625. https://doi.org/10.1016/j.agwat.2020.106625
Elbeltagi A, Deng J, Wang K, Hong Y (2020) Crop water footprint estimation and modeling using an artificial neural network approach in the Nile Delta. Egypt. Agric Water Manag 235:106080. https://doi.org/10.1016/j.agwat.2020.106080
Elbeltagi A, Azad N, Arshad A et al (2021) Egypt. Agric Water Manag 255:107052. https://doi.org/10.1016/j.agwat.2021.107052
Elbeltagi A, Raza A, Hu Y et al (2022c) Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration. Appl Water Sci 12:152. https://doi.org/10.1007/s13201-022-01667-7
Eryiğit M (2021) Estimation of parameters in groundwater modelling by modified Clonalg. J Hydroinform 23:298–306. https://doi.org/10.2166/hydro.2021.139
Feng Y, Cui N, Hao W et al (2019) Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338:67–77. https://doi.org/10.1016/j.geoderma.2018.11.044
Gadgil S, Vinayachandran PN, Francis PA (2003) Droughts of the Indian summer monsoon: role of clouds over the Indian Ocean. Curr Sci 85:1713–1719
Gebrehiwot T, van der Veen A, Maathuis B (2011) Spatial and temporal assessment of drought in the Northern highlands of Ethiopia. Int J Appl Earth Obs Geoinf 13:309–321. https://doi.org/10.1016/j.jag.2010.12.002
Gujja B, Dalai S, Shaik H, Goud V (2009) Adapting to climate change in the Godavari River basin of India by restoring traditional water storage systems. Clim Dev 1:229–240. https://doi.org/10.3763/cdev.2009.0020
Haghiabi AH, Nasrolahi AH, Parsaie A (2018) Water quality prediction using machine learning methods. Water Qual Res J 53:3–13. https://doi.org/10.2166/wqrj.2018.025
He X, Pan M, Wei Z et al (2020) A global drought and flood catalogue from 1950 to 2016. Bull Am Meteorol Soc 101:E508–E535. https://doi.org/10.1175/BAMS-D-18-0269.1
Karahan H, Ayvaz MT (2008) Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks. Hydrogeol J 16:817–827. https://doi.org/10.1007/s10040-008-0279-0
Kisi O, Shiri J (2012) River suspended sediment estimation by climatic variables implication: Comparative study among soft computing techniques. Comput Geosci 43:73–82. https://doi.org/10.1016/j.cageo.2012.02.007
Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58. https://doi.org/10.1016/j.jhydrol.2012.05.031
Kumar A, Kumar P, Singh VK (2019) Evaluating different machine learning models for runoff and suspended sediment simulation. Water Resour Manag 33:1217–1231. https://doi.org/10.1007/s11269-018-2178-z
Kumar KS, AnandRaj P, Sreelatha K, Sridhar V (2021) Regional analysis of drought severity-duration-frequency and severity-area-frequency curves in the Godavari River Basin, India. Int J Climatol 41:5481–5501. https://doi.org/10.1002/joc.7137
Kumar A, Singh VK, Saran B et al (2022a) Development of novel hybrid models for prediction of drought- and stress-tolerance indices in teosinte introgressed maize lines using artificial intelligence techniques. Sustainability 14:2287. https://doi.org/10.3390/su14042287
Kumar R, Kumar A, Shankhwar AK et al (2022) Modelling of meteorological drought in the foothills of Central Himalayas: a case study in Uttarakhand State. India. Ain Shams Eng J 13:101595. https://doi.org/10.1016/j.asej.2021.09.022
Kushwaha NL, Rajput J, Elbeltagi A et al (2021) Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: a case study from Northern India. Atmosphere (basel) 12:1654. https://doi.org/10.3390/atmos12121654
Kushwaha NL, Rajput J, Sena DR et al (2022) Evaluation of data-driven hybrid machine learning algorithms for modelling daily reference evapotranspiration. Atmos Ocean 60:519–540. https://doi.org/10.1080/07055900.2022.2087589
Lamorski K, Pachepsky Y, Sławiński C, Walczak RT (2008) Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Sci Soc Am J 72:1243–1247. https://doi.org/10.2136/sssaj2007.0280N
Li S, Xie Q, Yang J (2022) Daily suspended sediment forecast by an integrated dynamic neural network. J Hydrol 604:127258. https://doi.org/10.1016/j.jhydrol.2021.127258
Lohani AK, Goel NK, Bhatia KKS (2006) Takagi-Sugeno fuzzy inference system for modeling stage–discharge relationship. J Hydrol 331:146–160. https://doi.org/10.1016/j.jhydrol.2006.05.007
Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249:126169. https://doi.org/10.1016/j.chemosphere.2020.126169
Mahajan DR, Dodamani BM (2015) Trend analysis of drought events over Upper Krishna Basin in Maharashtra. Aquat Procedia 4:1250–1257. https://doi.org/10.1016/j.aqpro.2015.02.163
Mahajan DR, Dodamani BM (2016) Spatial and temporal drought analysis in the Krishna river basin of Maharashtra, India. Cogent Eng 3:1185926. https://doi.org/10.1080/23311916.2016.1185926
Malik R, Pande S, Nishi, Khamparia A (2020) Artificial intelligence and machine learning to assist climate change monitoring. J Artif Intell Syst 2:168–190. https://doi.org/10.33969/AIS.2020.21011
Malik A, Tikhamarine Y, Al-Ansari N et al (2021) Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Eng Appl Comput Fluid Mech 15:1075–1094. https://doi.org/10.1080/19942060.2021.1942990
Masinde M (2014) Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig Adapt Strateg Glob Chang 19:1139–1162. https://doi.org/10.1007/s11027-013-9464-0
Masroor M, Rehman S, Avtar R et al (2020) Exploring climate variability and its impact on drought occurrence: evidence from Godavari Middle sub-basin. India. Weather Clim Extrem 30:100277. https://doi.org/10.1016/j.wace.2020.100277
Maybank J, Bonsai B, Jones K et al (1995) Drought as a natural disaster. Atmos Ocean 33:195–222. https://doi.org/10.1080/07055900.1995.9649532
Mehdizadeh S, Fathian F, Safari MJS, Khosravi A (2020) Developing novel hybrid models for estimation of daily soil temperature at various depths. Soil Tillage Res 197:104513. https://doi.org/10.1016/j.still.2019.104513
Mohamadi S, Sammen SS, Panahi F et al (2020) Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Nat Hazards 104:537–579. https://doi.org/10.1007/s11069-020-04180-9
Mohammed R, Scholz M (2017) The reconnaissance drought index: a method for detecting regional arid climatic variability and potential drought risk. J Arid Environ 144:181–191. https://doi.org/10.1016/j.jaridenv.2017.03.014
Mohammed S, Elbeltagi A, Bashir B et al (2022) A comparative analysis of data mining techniques for agricultural and hydrological drought prediction in the eastern Mediterranean. Comput Electron Agric 197:106925. https://doi.org/10.1016/j.compag.2022.106925
Mokarram M, Zarei AR, Etedali HR (2021) Optimal location of yield with the cheapest water footprint of the crop using multiple regression and artificial neural network models in GIS. Theor Appl Climatol 143:701–712. https://doi.org/10.1007/s00704-020-03413-y
Mondol MAH, Ara I, Das SC (2017) Meteorological drought index mapping in Bangladesh using standardized precipitation index during 1981–2010. Adv Meteorol 2017:4642060. https://doi.org/10.1155/2017/4642060
Moreira EE, Coelho CA, Paulo AA et al (2008) SPI-based drought category prediction using loglinear models. J Hydrol 354:116–130. https://doi.org/10.1016/j.jhydrol.2008.03.002
Nguyen DT, Chen S-T (2020) Real-time probabilistic flood forecasting using multiple machine learning methods. Water 12:787. https://doi.org/10.3390/w12030787
Noymanee J, Theeramunkong T (2019) Flood forecasting with machine learning technique on hydrological modeling. Procedia Comput Sci 156:377–386. https://doi.org/10.1016/j.procs.2019.08.214
Noymanee J, Nikitin NO, Kalyuzhnaya AV (2017) Urban pluvial flood forecasting using open data with machine learning techniques in Pattani Basin. Procedia Comput Sci 119:288–297. https://doi.org/10.1016/j.procs.2017.11.187
Park J-H, Kim K-B, Chang H-Y (2014) Statistical properties of effective drought index (EDI) for Seoul, Busan, Daegu, Mokpo in South Korea. Asia-Pac J Atmos Sci 50:453–458. https://doi.org/10.1007/s13143-014-0035-4
Patel NR, Yadav K (2015) Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India. Nat Hazards 77:663–677. https://doi.org/10.1007/s11069-015-1614-0
Pittaki-Chrysodonta Z, Hartemink AE, Huang J (2021) Rapid estimation of a soil–water retention curve using visible–near infrared spectroscopy. J Hydrol 603:127195. https://doi.org/10.1016/j.jhydrol.2021.127195
Poonia V, Jha S, Goyal MK (2021) Copula based analysis of meteorological, hydrological and agricultural drought characteristics across Indian river basins. Int J Climatol 41:4637–4652. https://doi.org/10.1002/joc.7091
Praveen B, Talukdar S, Shahfahad, et al (2020) Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Sci Rep 10:10342. https://doi.org/10.1038/s41598-020-67228-7
Rajeevan M, Unnikrishnan CK, Bhate J et al (2012) Northeast monsoon over India: variability and prediction. Meteorol Appl 19:226–236. https://doi.org/10.1002/met.1322
Rezaie-Balf M, Zahmatkesh Z, Kim S (2017) Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm vs. model classification methods. Water Resour Manag 31:3843–3865. https://doi.org/10.1007/s11269-017-1711-9
Ridwan WM, Sapitang M, Aziz A et al (2021) Rainfall forecasting model using machine learning methods: case study Terengganu, Malaysia. Ain Shams Eng J 12:1651–1663. https://doi.org/10.1016/j.asej.2020.09.011
Roxy MK, Ritika K, Terray P et al (2015) Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nat Commun 6:7423. https://doi.org/10.1038/ncomms8423
Sadeghi-Tabas S, Samadi SZ, Akbarpour A, Pourreza-Bilondi M (2016) Sustainable groundwater modeling using single- and multi-objective optimization algorithms. J Hydroinform 19:97–114. https://doi.org/10.2166/hydro.2016.006
Sammen SS, Ehteram M, Abba SI et al (2021) A new soft computing model for daily streamflow forecasting. Stoch Environ Res Risk Assess 35:2479–2491. https://doi.org/10.1007/s00477-021-02012-1
Sandhu AK, Batth RS (2021) Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm. Softw Pract Exp 51:735–747. https://doi.org/10.1002/spe.2921
Shah R, Bharadiya N, Manekar V (2015) Drought index computation using standardized precipitation index (spi) method for Surat District, Gujarat. Aquat Procedia 4:1243–1249. https://doi.org/10.1016/j.aqpro.2015.02.162
Sharma A, Sen S (2021) Impact of drought on economy: a district level analysis of Madhya Pradesh, India. J Environ Plan Manag 64:1021–1043. https://doi.org/10.1080/09640568.2020.1797651
Sheffield J, Wood EF, Roderick ML (2012) Little change in global drought over the past 60 years. Nature 491:435–438. https://doi.org/10.1038/nature11575
Singh VK, Kumar D, Kashyap PS, Kisi O (2018) Simulation of suspended sediment based on gamma test, heuristic, and regression-based techniques. Environ Earth Sci 77:708. https://doi.org/10.1007/s12665-018-7892-6
Singh VK, Kumar D, Kashyap PS et al (2020) Modelling of soil permeability using different data driven algorithms based on physical properties of soil. J Hydrol 580:124223. https://doi.org/10.1016/j.jhydrol.2019.124223
Singh VK, Kumar D, Singh SK et al (2021) Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India. J Environ Manage 284:111985. https://doi.org/10.1016/j.jenvman.2021.111985
Singh AK, Kumar P, Ali R et al (2022a) An integrated statistical-machine learning approach for runoff prediction. Sustainability 14:8209. https://doi.org/10.3390/su14138209
Singh VK, Panda KC, Sagar A et al (2022b) Novel genetic algorithm (GA) based hybrid machine learning-pedotransfer function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity. Eng Appl Comput Fluid Mech 16:1082–1099. https://doi.org/10.1080/19942060.2022.2071994
Sinha D, Syed TH, Famiglietti JS et al (2017) Characterizing drought in India using GRACE observations of terrestrial water storage deficit. J Hydrometeorol 18:381–396. https://doi.org/10.1175/JHM-D-16-0047.1
Soh YW, Koo CH, Huang YF, Fung KF (2018) Application of artificial intelligence models for the prediction of standardized precipitation evapotranspiration index (SPEI) at Langat River Basin, Malaysia. Comput Electron Agric 144:164–173. https://doi.org/10.1016/j.compag.2017.12.002
Sun D, Kafatos M (2007) Note on the NDVI-LST relationship and the use of temperature-related drought indices over North America. Geophys Res Lett 34:L24406. https://doi.org/10.1029/2007GL031485
Tian P, Feng J, Zhao G et al (2022) Rainfall, runoff, and suspended sediment dynamics at the flood event scale in a Loess Plateau watershed. China. Hydrol Process 36:e14486. https://doi.org/10.1002/hyp.14486
Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the reconnaissance drought index (RDI). Water Resour Manag 21:821–833. https://doi.org/10.1007/s11269-006-9105-4
Van Dijk AIJM, Beck HE, Crosbie RS et al (2013) The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour Res 49:1040–1057. https://doi.org/10.1002/wrcr.20123
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
Wable PS, Jha MK, Shekhar A (2019) Comparison of drought indices in a semi-arid river basin of India. Water Resour Manag 33:75–102. https://doi.org/10.1007/s11269-018-2089-z
Witten IH, Frank E (2002) Data mining: practical machine learning tools and techniques with Java implementations. Acm Sigmod Rec 31:76–77
Yaseen ZM, Ebtehaj I, Bonakdari H et al (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol 554:263–276. https://doi.org/10.1016/j.jhydrol.2017.09.007
Yaseen ZM, Ali M, Sharafati A et al (2021) Forecasting standardized precipitation index using data intelligence models: regional investigation of Bangladesh. Sci Rep 11:3435. https://doi.org/10.1038/s41598-021-82977-9
Yu T, Yang S, Bai Y et al (2018) Inlet water quality forecasting of wastewater treatment based on kernel principal component analysis and an extreme learning machine. Water 10:873. https://doi.org/10.3390/w10070873
Zhang X, Yamaguchi Y, Li F et al (2017) Assessing the impacts of the 2009/2010 drought on vegetation indices, normalized difference water index, and land surface temperature in southwestern China. Adv Meteorol 2017:1–9. https://doi.org/10.1155/2017/6837493
Zuo D, Hou W, Wu H et al (2021) Feasibility of calculating standardized precipitation index with short-term precipitation data in China. Atmosphere (basel) 12:603. https://doi.org/10.3390/atmos12050603
Bates B, Kundzewicz Z, Wu S (2008a) Climate change and water. Intergovernmental Panel on Climate Change Secretariat
Dikshit A, Pradhan B, Alamri AM (2020) Short-Term spatio-temporal drought forecasting using random forests model at New South Wales, Australia. Appl Sci 10:. https://doi.org/10.3390/app10124254
Elbeltagi A, Kumar M, Kushwaha NL, et al (2022a) Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India. Stoch Environ Res Risk Assesshttps://doi.org/10.1007/s00477-022-02277-0
Elbeltagi A, Kushwaha NL, Rajput J et al (2022b) Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions. Stoch Environ Res Risk Assess https://doi.org/10.1007/s00477-022-02196-0
Gurara MA, Jilo NB, Tolche AD (2021) Modeling climate change impact on the streamflow in the Upper Wabe Bridge watershed in Wabe Shebele River Basin, Ethiopia. Int J River Basin Manag. https://doi.org/10.1080/15715124.2021.1935978
Hordofa AT, Leta OT, Alamirew T, et al (2021) Performance evaluation and comparison of satellite-derived rainfall datasets over the Ziway Lake Basin, Ethiopia. Climate 9:. https://doi.org/10.3390/cli9070113
Kavi Kumar KS (2021) Rice production systems and drought resilience in India BT - sustainable development insights from India: selected essays in honour of Ramprasad Sengupta. In: Dasgupta P, Saha AR, Singhal R (eds). Springer Singapore, Singapore, pp 303–316
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. Boston, Anaheim, California, pp 179–183
Mohammed S, Alsafadi K, Mousavi SMN, Harsányi E (2021) Rainfall change and spatial-temporal aspects of agricultural drought in Syria. In: Al-Maktoumi A, Abdalla O, Kacimov A, et al. (eds) Water Resources in Arid Lands: Management and Sustainability. Advances in Science, Technology & Innovation. Springer International Publishing, Cham, pp 215–221
Palmer WC (1965) Meteorological drought. U.S. Research Paper No. 45. US Department of Commerce, Weather Bureau
Payus C, Ann Huey L, Adnan F, et al (2020) Impact of Extreme drought climate on water security in North Borneo: case study of Sabah. Water 12:. https://doi.org/10.3390/w12041135
Saharwardi MS, Kumar P (2021) Future drought changes and associated uncertainty over the homogenous regions of India: a multimodel approach. Int J Climatol. https://doi.org/10.1002/joc.7265
Shukla R, Kumar P, Vishwakarma DK, et al (2021) Modeling of stage-discharge using back propagation ANN, ANFIS, and WANN-based computing techniques. TheorAppl Climatol.https://doi.org/10.1007/s00704-021-03863-y
Tyralis H, Papacharalampous G, Langousis A (2019) A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water 11:. https://doi.org/10.3390/w11050910
Vishwakarma DK, Ali R, Bhat SA, et al (2022a) Pre- and post-dam river water temperature alteration prediction using advanced machine learning models. Environ Sci Pollut Res.https://doi.org/10.1007/s11356-022-21596-x
Vishwakarma DK, Kumar R, Kumar A, et al (2022b) Evaluation and development of empirical models for wetted soil fronts under drip irrigation in high-density apple crop from a point source. Irrig Sci. https://doi.org/10.1007/s00271-022-00826-7
Zhongming Z, Linong L, Xiaona Y, et al (2012) Food security: near future projections of the impact of drought in Asia
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Ahmed Elbeltagi: development of ML models, formal analysis, and writing review and editing. Chaitanya B. Pande: original draft writing, discussion section, formal analysis, methodology, supervision, data collection and analysis for modeling purpose, processing of data, revision of paper, major work completed, response to all reviewer’s comments, main contribution in revision of paper, writing review and editing, investigation. Manish Kumar: writing results and discussion section and creating the Taylor diagrams and analysis. Abebe Debele Tolche: original draft writing, writing review, and editing.
Sudhir Kumar Singh, Akshay Kumar, and Dinesh Kumar Vishwakarma: writing review and editing.
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Elbeltagi, A., Pande, C.B., Kumar, M. et al. Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environ Sci Pollut Res 30, 43183–43202 (2023). https://doi.org/10.1007/s11356-023-25221-3
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DOI: https://doi.org/10.1007/s11356-023-25221-3