Miyan, M. A. Droughts in Asian least developed countries: Vulnerability and sustainability. Weather Clim. Extrem. 7, 11 (2015).
Campos, J. N. B. Paradigms and public policies on drought in northeast Brazil: A historical perspective. Environ. Manag. 55, 1052–1063 (2015).
Hao, Z. & Singh, V. P. Drought characterization from a multivariate perspective: A review. J. Hydrol. 527, 668–678 (2015).
Orimoloye, I. R., Belle, J. A., Orimoloye, Y. M., Olusola, A. O. & Ololade, O. O. Drought: A common environmental disaster. Atmosphere (Basel) 13, 111 (2022).
Thomas, J. & Prasannakumar, V. Temporal analysis of rainfall (1871–2012) and drought characteristics over a tropical monsoon-dominated State (Kerala) of India. J. Hydrol. 534, 266–280 (2016).
Mare, F., Bahta, Y. T. & Van Niekerk, W. The impact of drought on commercial livestock farmers in South Africa. Dev. Pract. 28, 884–898 (2018).
Afrin, S. Livelihood Pattern of Flooded Families in Bangladesh: A Study in Two Villages. 1–131 (2015).
Rahman, M. R. & Lateh, H. Meteorological drought in Bangladesh: Assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data. Environ. Earth Sci. 75, 22 (2016).
Habiba, U., Shaw, R. & Takeuchi, Y. Drought risk reduction through a socio-economic, institutional and physical approach in the northwestern region of Bangladesh. Environ. Hazards 10, 31 (2011).
Mortuza, M. R., Moges, E., Demissie, Y. & Li, H.-Y. Historical and future drought in Bangladesh using copula-based bivariate regional frequency analysis. Theor. Appl. Climatol. 135, 855–871 (2019).
Mishra, A. K. & Singh, V. P. Drought modeling—A review. J. Hydrol. 403, 157–175 (2011).
Mohsenipour, M., Shahid, S., Chung, E. & Wang, X. Changing pattern of droughts during cropping seasons of Bangladesh. Water Resour. Manag. 32, 3 (2018).
Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N. & Shahid, S. Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Sci. Rep. 11, 1–25 (2021).
Bhaga, T. D., Dube, T., Shekede, M. D. & Shoko, C. Impacts of climate variability and drought on surface water resources in sub-Saharan Africa using remote sensing: A review. Remote Sens. 12, 33 (2020).
Vicente-Serrano, S. M., Beguer’ia, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).
Mondol, M. A. H., Ara, I. & Das, S. C. Meteorological drought index mapping in Bangladesh using standardized precipitation index during 1981–2010. Adv. Meteorol. 2017, 4642060 (2017).
Al-Mamun, A. et al. Identification of meteorological drought prone area in Bangladesh using standardized precipitation index. J. Earth Sci. Clim. Change 9, 1000457 (2018).
Moazzam, M. F. U., Rahman, G., Munawar, S., Farid, N. & Lee, B. G. Spatiotemporal rainfall variability and drought assessment during past five decades in South Korea using SPI and SPEI. Atmosphere (Basel) 13, 292 (2022).
Liu, C., Yang, C., Yang, Q. & Wang, J. Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China. Sci. Rep. 11, 1–14 (2021).
Ojha, S. S., Singh, V. & Roshni, T. Comparison of meteorological drought using SPI and SPEI. Civ. Eng. J. 7, 2130–2149 (2021).
Wang, H., Chen, Y., Pan, Y., Chen, Z. & Ren, Z. Assessment of candidate distributions for SPI/SPEI and sensitivity of drought to climatic variables in China. Int. J. Climatol. 39, 4392–4412 (2019).
McKee, T. B. Drought monitoring with multiple time scales. In Proceedings of 9th Conference on Applied Climatology, Boston, 1995 (1995).
Botai, C. M., Botai, J. O., Dlamini, L. C., Zwane, N. S. & Phaduli, E. Characteristics of droughts in South Africa: A case study of free state and north west provinces. Water 8, 133 (2016).
Temoso, O., Villano, R. A. & Hadley, D. Agricultural productivity, efficiency and growth in a semi-arid country: A case study of Botswana, African. J. Agric. Resour. Econ. 10, 192–206 (2015).
Bhunia, P., Das, P. & Maiti, R. Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Syst. Environ. 4, 43–55 (2020).
Naresh Kumar, M., Murthy, C. S., Sesha Sai, M. V. R. & Roy, P. S. On the use of standardized precipitation index (SPI) for drought intensity assessment. Meteorol. Appl. J. Forecast. Pract. Appl. Train. Tech. Model. 16, 381–389 (2009).
Danandeh Mehr, A., Sorman, A. U., Kahya, E. & Hesami Afshar, M. Climate change impacts on meteorological drought using SPI and SPEI: Case study of Ankara, Turkey. Hydrol. Sci. J. 65, 254–268 (2020).
Li, L., She, D., Zheng, H., Lin, P. & Yang, Z.-L. Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. J. Hydrometeorol. 21, 1513–1530 (2020).
Lee, S.-H., Yoo, S.-H., Choi, J.-Y. & Bae, S. Assessment of the impact of climate change on drought characteristics in the Hwanghae Plain, North Korea using time series SPI and SPEI: 1981–2100. Water 9, 579 (2017).
Chen, S., Zhang, L., Liu, X., Guo, M. & She, D. The use of SPEI and TVDI to assess temporal-spatial variations in drought conditions in the middle and lower reaches of the Yangtze River Basin, China. Adv. Meteorol. 2018, 9362041 (2018).
Bae, S., Lee, S.-H., Yoo, S.-H. & Kim, T. Analysis of drought intensity and trends using the modified SPEI in South Korea from 1981 to 2010. Water 10, 327 (2018).
Jia, Y., Zhang, B. & Ma, B. Daily SPEI reveals long-term change in drought characteristics in Southwest China. Chinese Geogr. Sci. 28, 680–693 (2018).
Yang, M., Yan, D., Yu, Y. & Yang, Z. SPEI-based spatiotemporal analysis of drought in Haihe River Basin from 1961 to 2010. Adv. Meteorol. 2016, 7658015 (2016).
Ye, L. et al. Spatio-temporal analysis of drought indicated by SPEI over northeastern China. Water 11, 908 (2019).
Musei, S. K., Nyaga, J. M. & Dubow, A. Z. SPEI-based spatial and temporal evaluation of drought in Somalia. J. Arid Environ. 184, 104296 (2021).
Mutsotso, R. B., Sichangi, A. W. & Makokha, G. O. Spatio-Temporal Drought Characterization in Kenya from 1987 to 2016 (2018).
Alam, N. M. et al. Evaluation of drought using SPEI drought class transitions and log-linear models for different agro-ecological regions of India. Phys. Chem. Earth Parts A/B/C 100, 31–43 (2017).
Bera, B., Shit, P. K., Sengupta, N., Saha, S. & Bhattacharjee, S. Trends and variability of drought in the extended part of Chhota Nagpur plateau (Singbhum Protocontinent), India applying SPI and SPEI indices. Environ. Challenges 5, 100310 (2021).
Dai, A. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. J. Geophys. Res. Atmos. 116, 21 (2011).
Karl, T. R. The sensitivity of the Palmer Drought Severity Index and Palmer’s Z-index to their calibration coefficients including potential evapotranspiration. J. Clim. Appl. Meteorol. 2, 77–86 (1986).
Tan, C., Yang, J. & Li, M. Temporal-spatial variation of drought indicated by SPI and SPEI in Ningxia Hui Autonomous Region, China. Atmosphere (Basel) 6, 1399–1421 (2015).
Potop, V., Možný, M. & Soukup, J. Drought evolution at various time scales in the lowland regions and their impact on vegetable crops in the Czech Republic. Agric. For. Meteorol. 156, 121–133 (2012).
Mokhtar, A. et al. Estimation of SPEI meteorological drought using machine learning algorithms. IEEE Access 9, 65503–65523 (2021).
Abdel-Fattah, M. K., Mokhtar, A. & Abdo, A. I. Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: A case study from Egypt. Environ. Sci. Pollut. Res. 28, 898–914 (2021).
Mossad, A. & Alazba, A. A. Drought forecasting using stochastic models in a hyper-arid climate. Atmosphere (Basel). 6, 410–430 (2015).
Zhang, R., Chen, Z. Y., Xu, L. J. & Ou, C. Q. Meteorological drought forecasting based on a statistical model with machine learning techniques in Shaanxi province, China. Sci. Total Environ. 665, 31 (2019).
Elbeltagi, A. et al. The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt. Sci. Total Environ. 743, 140770 (2020).
Maroufpoor, S., Maroufpoor, E., Bozorg-Haddad, O., Shiri, J. & Mundher Yaseen, Z. Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J. Hydrol. 575, 544–556 (2019).
Ganguli, P. & Reddy, M. J. Ensemble prediction of regional droughts using climate inputs and the SVM-copula approach. Hydrol. Process. 28, 4989–5009 (2014).
Feng, Y., Cui, N., Chen, Y., Gong, D. & Hu, X. Development of data-driven models for prediction of daily global horizontal irradiance in northwest China. J. Clean. Prod. 223, 136–146 (2019).
Granata, F. Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agric. Water Manag. 217, 303–315 (2019).
Mouatadid, S., Raj, N., Deo, R. C. & Adamowski, J. F. 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 (2018).
Masinde, M. Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability. Mitig. Adapt. Strategy Glob. Chang. 19, 1139–1162 (2014).
Belayneh, A., Adamowski, J., Khalil, B. & Ozga-Zielinski, B. 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 (2014).
Djerbouai, S. & Souag-Gamane, D. 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 (2016).
Deo, R. C. & Şahin, M. Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos. Res. 3, 161–162 (2015).
Anshuka, A., van Ogtrop, F. F. & Willem Vervoort, R. Drought forecasting through statistical models using standardised precipitation index: A systematic review and meta-regression analysis. Nat. Hazards 97, 65 (2019).
Ahmed, K., Shahid, S., Chung, E.-S., Wang, X. & Harun, S. B. Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan. J. Hydrol. 570, 473–485 (2019).
Elbeltagi, A. et al. Estimating the standardized precipitation evapotranspiration index using data-driven techniques: A regional study of Bangladesh. Water (Switzerland) 14, 1–16 (2022).
Kafy, A. et al. Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2023.161394 (2023).
Osmani, S. A. et al. Prediction of monthly dry days with machine learning algorithms: A case study in Northern Bangladesh. Sci. Rep. 12, 1–21 (2022).
Rahman, M. R. & Lateh, H. Climate change in Bangladesh: A spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theor. Appl. Climatol. 128, 27–41 (2017).
Shahid, S. Recent trends in the climate of Bangladesh. Clim. Res. 42, 185–193 (2010).
Pereira, J. J., Pulhin, J. M. & Shaw, R. Climate Change Adaptation and Disaster Risk Reduction: An Asian Perspective (Emerald Group Pub. Limited, 2010).
Dastagir, M. R. Modeling recent climate change induced extreme events in Bangladesh: A review. Weather Clim. Extrem. 7, 49–60 (2015).
Abedin, M. A., Collins, A. E., Habiba, U. & Shaw, R. Climate change, water scarcity, and health adaptation in southwestern coastal Bangladesh. Int. J. Disaster Risk Sci. 10, 28–42 (2019).
Pande, C. B., Al-ansari, N., Kushwaha, N. L., Srivastava, A. & Noor, R. Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree. 1–24 (2022).
Buttafuoco, G., Caloiero, T. & Coscarelli, R. Analyses of drought events in Calabria (southern Italy) using standardized precipitation index. Water Resour. Manag. 29, 557–573 (2015).
Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M. & Gentine, P. Machine Learning for Clouds and Climate (Clouds Climate Cambridge University Press Cambridge, 2020).
Mullick, M. R. A., Nur, M. R. M., Alam, M. J. & Islam, K. M. A. Observed trends in temperature and rainfall in Bangladesh using pre-whitening approach. Glob. Planet. Change https://doi.org/10.1016/j.gloplacha.2018.10.001 (2019).
Chiew, F. H. S., Kamaladasa, N. N., Malano, H. M. & McMahon, T. A. Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia. Agric. Water Manag. 28, 9–21 (1995).
Mokhtar, A. et al. Evapotranspiration as a response to climate variability and ecosystem changes in southwest, China. Environ. Earth Sci. 79, 312 (2020).
Li, Y., Ren, F., Li, Y., Wang, P. & Yan, H. Characteristics of the regional meteorological drought events in Southwest China during 1960–2010. J. Meteorol. Res. 28, 381–392 (2014).
Jiang, W., Wang, L., Feng, L., Zhang, M. & Yao, R. Drought characteristics and its impact on changes in surface vegetation from 1981 to 2015 in the Yangtze River Basin, China. Int. J. Climatol. 40, 3380–3397 (2020).
Nafarzadegan, A. R. et al. Drought area monitoring during the past three decades in Fars province, Iran. Quat. Int. 250, 27–36 (2012).
Banimahd, S. A. & Khalili, D. Factors influencing Markov chains predictability characteristics, utilizing SPI, RDI, EDI and SPEI drought indices in different climatic zones. Water Resour. Manag. 27, 3911–3928 (2013).
Spinoni, J., Naumann, G., Carrao, H., Barbosa, P. & Vogt, J. World drought frequency, duration, and severity for 1951–2010. Int. J. Climatol. 34, 2792–2804 (2014).
Bouktif, S., Fiaz, A., Ouni, A. & Serhani, M. A. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 11, 22 (2018).
Khan, M. J. U. et al. Observed trends in climate extremes over Bangladesh from 1981 to 2010. Clim. Res. 77, 45–61 (2019).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Martinez-Taboada, F. & Redondo, J. I. Variable importance plot (mean decrease accuracy and mean decrease Gini). PLoS One 15, e0230799 (2020).
Han, H., Guo, X. & Yu, H. Variable selection using mean decrease accuracy and mean decrease Gini based on random forest. Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS 7, 219–224 (2016).
Singh, U. K., Jamei, M., Karbasi, M., Malik, A. & Pandey, M. Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture. J. Hydrol. 607, 127549 (2022).
Janizadeh, S. et al. Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. J. Environ. Manag. 298, 113551 (2021).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning. Vol. 112 (Springer, 2013).
Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).
Ebrahimi-Khusfi, Z., Nafarzadegan, A. R. & Dargahian, F. Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques. Ecol. Indic. 125, 107499 (2021).
Kuhn, M. et al. Classification and regression tree. Environ. Ecol. Stat. R https://doi.org/10.1201/b17172-15 (2021).
Chen, Y. et al. Toward the development of deep learning analyses for snow avalanche releases in mountain regions. Geocarto Int. 3, 1–26 (2021).
Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H. R. & Van Oost, K. Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci. Total Environ. 664, 1117–1132 (2019).
Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H. & Chau, K. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Sci. Total Environ. 631–632, 1279–1294 (2018).
Greenwell, B. M. pdp: An R package for constructing partial dependence plots. R J. 9, 421–436 (2017).
Goldstein, A., Kapelner, A., Bleich, J. & Pitkin, E. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 24, 44–65 (2015).
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 106, 7183–7192 (2001).
Heim, R. R. A review of twentieth-century drought indices used in the United States. Bull. Am. Meteorol. Soc. 83, 31 (2002).
Alamgir, M. et al. Parametric assessment of seasonal drought risk to crop production in Bangladesh. Sustainable 11, 1–17 (2019).
Khan, N. et al. Prediction of droughts over Pakistan using machine learning algorithms. Adv. Water Resour. 139, 103562 (2020).
Kamruzzaman, M., Jang, M. W., Cho, J. & Hwang, S. Future changes in precipitation and drought characteristics over Bangladesh under CMIP5 climatological projections. Water (Switzerland) 11, 291 (2019).
Wang, Z. et al. Does drought in China show a significant decreasing trend from 1961 to 2009?. Sci. Total Environ. 579, 4 (2017).
Mishra, A. & Liu, S. C. Changes in precipitation pattern and risk of drought over India in the context of global warming. J. Geophys. Res. 119, 13 (2014).
Ge, Y., Cai, X., Zhu, T. & Ringler, C. Drought frequency change: An assessment in northern India plains. Agric. Water Manag. 176, 111 (2016).
Afzal, M. & Ragab, R. Drought risk under climate and land use changes: Implication to water resource availability at catchment scale. Water 11, 1–29 (2019).
Sajikumar, N. & Sobhana, R. Impact of land cover and land use change on runoff characteristics. J. Environ. Manag. 161, 460 (2015).
MoEF. Bangladesh Climate Change Strategy and Action Plan 2009. http://nda.erd.gov.bd/files/1/Publications/CCPolicyDocuments/BCCSAP2009.pdf (2009).
Arora, A., Bansal, S. & Ward, P. S. Do farmers value rice varieties tolerant to droughts and floods? Evidence from a discrete choice experiment in Odisha. India. Water Resour. Econ. 25, 27–41 (2019).
Dar, M. H. et al. Drought tolerant rice for ensuring food security in eastern India. Sustainability 12, 6 (2020).
Nayak, S. et al. Adoption trend of climate-resilient rice varieties in Bangladesh. Sustainability 14, 5156 (2022).
Ahmed, B. et al. Indigenous people’s responses to drought in northwest Bangladesh. Environ. Dev. 29, 4 (2019).
Shayanmehr, S. et al. The impacts of climate change on water resources and crop production in an arid region. Agriculture 12, 1056 (2022).
Easterling, D. R. et al. Climate extremes: observations, modeling, and impacts. Science (80-) 289, 2068–2074 (2000).
Verdon-Kidd, D. C. & Kiem, A. S. Nature and causes of protracted droughts in southeast Australia: Comparison between the federation, WWII, and big dry droughts. Geophys. Res. Lett. 36, 22 (2009).
Choruma, D. J., Akamagwuna, F. C. & Odume, N. O. Simulating the impacts of climate change on maize yields using EPIC: A case study in the Eastern Cape Province of South Africa. Agriculture 12, 794 (2022).
Auffhammer, M., Hsiang, S. M., Schlenker, W. & Sobel, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy (2013).
Assunção, J. & Chein, F. Climate change and agricultural productivity in Brazil: Future perspectives. Environ. Dev. Econ. 21, 581–602 (2016).
Gettliffe, E. UN OCHA Anticipatory Action. Lessons from the 2020 Somalia Pilot. https//reliefweb.int/report/somalia/un-ochaanticipatory-action-lessons-2020-somalia-pilot (Center Disaster Protection London, 2021).
Kassie, B. T. Climate Variability and Change in Ethiopia : Exploring Impacts and Adaptation Options for Cereal Production (2014).
Hao, Z., Singh, V. P. & Xia, Y. Seasonal drought prediction: Advances, challenges, and future prospects. Rev. Geophys. 56, 108–141 (2018).
Notton, G. & Voyant, C. Chapter 3—Forecasting of Intermittent Solar Energy Resource. (eds. Yahyaoui, I. B. T.-A. in R. E. and P. T.). 77–114. https://doi.org/10.1016/B978-0-12-812959-3.00003-4 (Elsevier, 2018).
Jiang, D. & Wang, K. The role of satellite-based remote sensing in improving simulated streamflow: A review. Water 11, 23 (2019).
Madadgar, S. et al. A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States. Water Resour. Res. 52, 34 (2016).
Slater, L. et al. Hybrid forecasting: Blending climate predictions with AI models. Hybrid Forecast. https://doi.org/10.5194/hess-27-1865-2023 (2023).
Breiman, L. & Freedman, D. How many variables should be entered in a regression equation?. J. Am. Stat. Assoc. 78, 131–136 (1983).
Breiman, L. & Ihaka, R. Nonlinear discriminant analysis via scaling and ACE. In Technical Report No. 40 (1984).
Therneau, T., Atkinson, B. & Ripley, B. Recursive Partitioning and Regression Trees (2022).
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19, 281 (2019).
Noble, W. S. What is a support vector machine?. Nat. Biotechnol. 24, 1565–1567 (2006).
Kober, J., Bagnell, J. A. & Peters, J. Reinforcement learning in robotics: A survey. Int. J. Rob. Res. 32, 1238–1274 (2013).
Freedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).
Guelman, L. Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst. Appl. 39, 3659–3667 (2012).
Ridgeway, G. gbm—Generalized Boosted Models. R Package. 1–15 (2017).
Annette J. Dobson, A. G. B. An Introduction to Generalized Linear Models. https://doi.org/10.1201/9781315182780 (Chapman and Hall/CRC, 2018).
Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).
Seegrist, D. E. & Hilt, D. W. Ridge, a Computer Program for Calculating Ridge Regression Estimates (1977).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
Efron, B. et al. Least angle regression. Ann. Stat. 32, 407–499 (2004).
Dey, D. K., Ghosh, S. K. & Mallick, B. K. Generalized Linear Models: A Bayesian Perspective (CRC Press, 2000).
Chen, T. Introduction to boosted trees. Univ. Washington Comput. Sci. 22, 14–40 (2014).
Levshina, N. Conditional inference trees and random forests. In A Practical Handbook of Corpus Linguistics. 611–643 (Springer, 2020).
Sarda-Espinosa, A., Subbiah, S. & Bartz-Beielstein, T. Conditional inference trees for knowledge extraction from motor health condition data. Eng. Appl. Artif. Intell. 62, 26–37 (2017).
Olusegun, A. M., Dikko, H. G. & Gulumbe, S. U. Identifying the limitation of stepwise selection for variable selection in regression analysis. Am. J. Theor. Appl. Stat. 4, 414–419 (2015).
Friedman, J. H. & Roosen, C. B. An introduction to multivariate adaptive regression splines. Stat. Methods Med. Res. 4, 197–217 (1995).
Milborrow, S., Hastie, T. & Tibshirani, R. Earth: Multivariate Adaptive Regression Spline Models. R Package Version. Vol. 3 (2014).
Ripley, B. D. Pattern Recognition and Neural Networks (Cambridge University Press, 2007).
Farikha, E. F., Hadi, A. F., Anggraeni, D. & Riski, A. Projection pursuit regression in statistical downscaling model using artificial neural network for rainfall prediction. J. Phys. Conf. Ser. 1872, 12021 (2021).
Wright, M. N., Wager, S. & Probst, P. Ranger: A Fast Implementation of Random Forests. R Package version 0.12. Vol. 1 (2020).
Li, R. & Peng, L. Quantile regression for left-truncated semicompeting risks data. Biometrics 67, 701–710 (2011).
Bello, R. W., Mohamed, A. S. A., Talib, A. Z., Sani, S. & Ab Wahab, M. N. Behavior recognition of group-ranched cattle from video sequences using deep learning. Indian J. Anim. Res. 56, 505–512 (2022).
Ma, H., Li, T., Zhu, H. & Zhu, Z. Quantile regression for functional partially linear model in ultra-high dimensions. Comput. Stat. Data Anal. 129, 135–147 (2019).
Ciner, C., Lucey, B. & Yarovaya, L. Determinants of cryptocurrency returns: A LASSO quantile regression approach. Financ. Res. Lett. 49, 102990 (2022).
