Kanwar, N., Kuniyal, J. C., Rautela, K. S., Singh, L. & Pandey, D. C. Longitudinal assessment of extreme climate events in Kinnaur district, Himachal pradesh, north-western himalaya, India. Environ. Monit. Assess. 196 (6), 557 (2024).
Google Scholar
Ghiat, I., Mackey, H. R. & Al-Ansari, T. A review of evapotranspiration measurement models, techniques and methods for open and closed agricultural field applications. Water 13 (18), 2523 (2021).
Google Scholar
Malik, A. et al. Modeling monthly pan evaporation process over the Indian central himalayas: application of multiple learning artificial intelligence model. Eng. Appl. Comput. Fluid Mech. 14 (1), 323–338 (2020).
Pande C. B. et al. Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation. Environ Sci Eur. 36, 84 (20214).
Google Scholar
Qasem, S. N. et al. Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Eng. Appl. Comput. Fluid Mech. 13 (1), 177–187 (2019).
Shabani, S. et al. Modeling pan evaporation using Gaussian process regression k-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere 11 (1), 66 (2020).
Google Scholar
Ghorbani, M. A., Deo, R. C., Yaseen, Z. M., Kashani, M. H. & Mohammadi, B. Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoret. Appl. Climatol. 133, 1119–1113 (2017).
Google Scholar
Majhi, B., Naidu, D., Mishra, A. P. & Satapathy, S. C. Improved prediction of daily pan evaporation using deep-LSTM model. Neural Comput. Appl. 32 (12), 7823–7783 (2020).
Google Scholar
Wu, L. et al. Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput. Electron. Agric. 168, 105115 (2020).
Masoner, D. I. Differences in evaporation between a floating pan and class a pan on land 1. J. Am. Water Resour. Assoc. 44 (3), 552–556 (2008).
Kişi, Ö. Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol. Processes: Int. J. 23 (2), 213–223 (2009).
Google Scholar
Ashrafzadeh, A., Ghorbani, M. A., Biazar, S. M. & Yaseen, Z. M. Evaporation process modelling over Northern iran: application of an integrative data-intelligence model with the Krill herd optimization algorithm. Hydrol. Sci. J. 64 (15), 1843 –185 (2019).
Lu, X. et al. Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models. J. Hydrol. 566, 668–684 (2018).
Google Scholar
Al-Mukhtar, M. Modeling of pan evaporation based on the development of machine learning methods. Theoret. Appl. Climatol. 146 (3), 961–979 (2021).
Google Scholar
Salih, S. Q. et al. Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser lake in Egypt. Eng. Appl. Comput. Fluid Mech. 13 (1), 878–891 (2019).
Khan, N., Shahid, S., Ismail, T. & Wang, X. J. Spatial distribution of unidirectional trends in temperature and temperature extremes in Pakistan. Theoret. Appl. Climatol. 136 (3–4), 899–913 (2018).
Naganna, S. R. et al. Dew point temperature estimation: application of artificial intelligence model integrated with nature-inspired optimization algorithms. Water 11 (4), 742 (2019).
Google Scholar
Abdollahpour, S., Kosari-Moghaddam, A. & Bannayan, M. Prediction of wheat moisture content at harvest time through ANN and SVR modeling techniques. Inform. Process. Agric. 7 (4), 500–551 (2020).
Khan, N. et al. Prediction of droughts over Pakistan using machine learning algorithms. Adv. Water Resour. 139, 103562 (2020).
Google Scholar
Jehanzaib, M., Bilal Idrees, M., Kim, D. & Kim, T. W. Comprehensive evaluation of machine learning techniques for hydrological drought forecasting. J. Irrig. Drain. Eng. 147 (7), 0402102 (2021).
Google Scholar
Salih, S. Q. et al. Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments. Hydrol. Sci. J. 65 (7), 1145–1157 (2020).
Google Scholar
Elshaboury, N., Elshourbagy, M., Al-Sakkaf, A. & Abdelkader, E. M. Rainfall forecasting in arid regions using an ensemble of artificial neural networks. J. Phys: Conf. Ser. 1900, 012015 (2021).
Elbeltagi, A. et al. Data intelligence and hybrid metaheuristic algorithms-based Estimation of reference evapotranspiration. Appl. Water Sci. 12 (7), 1–18 (2022).
Google Scholar
Kushwaha, N. L. et al. Evaluation of data-driven hybrid machine learning algorithms for modelling daily reference evapotranspiration. Atmos. Ocean. 60 (5), 1–22 (2022).
Google Scholar
Rezaie-Balf, M. et al. Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: comparative assessment of a noise suppression hybridization approach. J. Clean. Prod. 271, 122576 (2020).
Google Scholar
Ghimire, S. et al. Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks. Sci. Rep. 11 (1), 1–26 (2021).
Google Scholar
He, S. et al. Machine learning improvement of streamflow simulation by utilizing remote sensing data and potential application in guiding reservoir operation. Sustainability 13 (7), 3645 (2021).
Google Scholar
Chia, M. Y., Huang, Y. F. & Koo, C. H. Support vector machine enhanced empirical reference evapotranspiration Estimation with limited meteorological parameters. Comput. Electron. Agric. 175, 105577 (2020).
Google Scholar
Malik, A. et al. Deep learning versus gradient boosting machine for pan evaporation prediction. Eng. Appl. Comput. Fluid Mech. 16 (1), 570–587 (2022).
Mehr, A. D. et al. Genetic programming in water resources engineering: A state-of-the-art review. J. Hydrol. 566, 643–667 (2018).
Google Scholar
Jing, W. et al. Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions. Eng. Appl. Comput. Fluid Mech. 13 (1), 811–823 (2019).
Al-Mukhtar, M. Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad. Environ. Monit. Assess. 191 (11), 1–12 (2019).
Google Scholar
Adnan, R. M., Malik, A., Kumar, A., Parmar, K. S. & Kisi, O. Pan evaporation modeling by three different neurofuzzy intelligent systems using Climatic inputs. Arab. J. Geosci. 12 (20), 60 (2019).
Ghaemi, A., Rezaie-Balf, M., Adamowski, J., Kisi, O. & Quilty, J. On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agric. For. Meteorol. 278, 107647 (2019).
Google Scholar
Khosravi, K. et al. Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Comput. Electron. Agric. 167, 105041 (2019).
Google Scholar
Kisi, O. & Heddam, S. Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol. Sci. J. 64 (6), 653–672 (2019).
Google Scholar
Rezaie-Balf, M., Kisi, O. & Chua, L. H. C. Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation. Hydrol. Res. 50 (2), 498–451 (2019).
Google Scholar
Sebbar, A., Heddam, S. & Djemili, L. Predicting daily pan evaporation (E pan) from dam reservoirs in the mediterranean regions of algeria: OPELM VS OSELM. Environ. Processes. 6 (1), 309–331 (2019).
Google Scholar
Alsumaiei, A. A. Utility of artificial neural networks in modeling pan evaporation in hyper-arid climates. Water 12 (5), 1508 (2020).
Google Scholar
Yaseen, Z. M. et al. Prediction of evaporation in arid and semi-arid regions: A comparative study using different machine learning models. Eng. Appl. Comput. Fluid Mech. 14 (1), 70–78 (2020).
Abed, M., Imteaz, M. A., Ahmed, A. N. & Huang, Y. F. Modelling monthly pan evaporation utilising random forest and deep learning algorithms. Sci. Rep. 12 (1), 13132 (2022).
Google Scholar
Ehteram, M., Graf, R., Ahmed, A. N. & El-Shafie, A. Improved prediction of daily pan evaporation using bayesian model averaging and optimized kernel extreme machine models in different climates. Stoch. Env. Res. Risk Assess. 36 (11), 3875–3910 (2022).
Google Scholar
Novotná, B. et al. Machine learning for pan evaporation modeling in different agroclimatic zones of the Slovak Republic (Macro-Regions). Sustainability 14 (6), 347 (2022).
Google Scholar
El Bilali, A. et al. An interpretable machine learning approach based on DNN, SVR, extra tree, and XGBoost models for predicting daily pan evaporation. J. Environ. Manage. 327, 116890 (2023).
Google Scholar
Elbeltagi, A., Al-Mukhtar, M., Kushwaha, N. L., Al-Ansari, N. & Vishwakarma, D. K. Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment. Appl. Water Sci. 13 (2), 4 (2023).
Google Scholar
Fu, T. & Li, X. Estimating the monthly pan evaporation with limited Climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms. Sci. Rep. 13 (1), 5960 (2023).
Google Scholar
Mohammed, A. S., Al-Hadeethi, B. & Almawla, A. S. Daily evapotranspiration prediction at arid and semiarid regions by using multiple linear regression technique at Ramadi City in Iraq region. IOP Conf. Series: Earth Environ. Sci. 1222, p012033 (2023).
Rajput, J. et al. Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India. Water Pract. Technol. 1, 2024144 (2024).
Hinton, G. E., Osindero, S. & Teh, Y. W. A fast learning algorithm for deep belief Nets. Neural Comput. 18 (7), 1527–1554 (2006).
Google Scholar
Achieng, K. O. Modelling of soil moisture retention curve using machine learning techniques: artificial and deep neural networks vs support vector regression models. Comput. Geosci. 133, 104320 (2019).
Google Scholar
Miikkulainen, R. et al. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing 293–312 (Academic Press, 2019).
Cortes, C. & Vapnik, V. Support-vector networks. Mach. Learn. 20 (3), 273–229 (1995).
Google Scholar
Tan, Y. V. & Roy, J. Bayesian additive regression trees and the general BART model. Stat. Med. 38 (25), 5048–5069 (2019).
Google Scholar
Sparapani, R., Spanbauer, C. & McCulloch, R. Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package. J. Stat. Softw. 97, 1–66 (2021).
Google Scholar
o, T. K. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20 (8), 832–844 (1998).
Google Scholar
Lasota, T., Łuczak, T., Niemczyk, M., Olszewski, M. & Trawiński, B. Investigation of property valuation models based on decision tree ensembles built over noised data. In International Conference on Computational Collective Intelligence 417–426 (Springer, 2013).
Skurichina, M. & Duin, R. P. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal. Appl. 5 (2), 121–135 (2002).
Google Scholar
Quinlan, J. R. Learning with continuous classes. In 5th Australian Joint Conference on Artificial Intelligence, vol. 92, 343–348 (1992).
Wang, Y. & Witten, I. H. Induction of Model Trees for Predicting Continuous Classes (University of Waikat, 1996).
Franklin, J. The elements of statistical learning: data mining, inference and prediction. Math. Intelligencer. 27 (2), 83–85 (2005).
Google Scholar
Breiman, L. Random forests. Mach. Learn. 45 (1), 5–32 (2001).
Google Scholar
Shi, T. & Horvath, S. Unsupervised learning with random forest predictors. J. Comput. Graphical Stat. 15 (1), 118–138 (2006).
Google Scholar
Niemeyer, J., Rottensteiner, F. & Soergel, U. Contextual classification of lidar data and Building object detection in urban areas. ISPRS J. Photogrammetry Remote Sens. 87, 152–165 (2014).
Google Scholar
Legates, D. R. & McCave, G. J. Jr. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35 (1), 233–241 (1999).
Google Scholar
Chai, T. & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7 (3), 1247–1250 (2014).
Google Scholar
Tao, H. et al. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions. Eng. Appl. Artif. Intell. 129, 107559 (2024).
Google Scholar
Samantaray, S. & Sahoo, A. Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey Wolf optimisation. Groundw. Sustainable Dev. 26, 101178 (2024).
Google Scholar
Al-Mukhtar, M., Srivastava, A., Khadke, L., Al-Musawi, T. & Elbeltagi, A. Prediction of irrigation water quality indices using random committee, discretization regression, reptree, and additive regression. Water Resour. Manage. 38 (1), 343–368 (2024).
Google Scholar
Samantaray, S., Sahoo, A. & Baliarsingh, F. Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm. Clean. Water. 1, 100003 (2024).
Google Scholar
Hameed, M. M. et al. Investigating a hybrid extreme learning machine coupled with Dingo optimization algorithm for modeling liquefaction triggering in sand-silt mixtures. Sci. Rep. 14 (1), 10799 (2024).
Google Scholar
Mahakur, V., Mahakur, V. K., Samantaray, S. & Ghose, D. K. Prediction of runoff at ungauged areas employing interpolation techniques and deep learning algorithm. HydroResearch 8, 265–275 (2025).
Google Scholar
Samantaray, S., Sahoo, A., Yaseen, Z. M. & Al-Suwaiyan, M. S. River discharge prediction based multivariate Climatological variables using hybridized long short-term memory with nature inspired algorithm. J. Hydrol. 649, 132453 (2025).
Google Scholar
Tulla et al. Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand. Theor Appl Climatol. 155, 4023–4047 (2024).
Google Scholar
Elbeltagi et al. Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments. Agric. Water Manag. 283, 108302 (2023).
Google Scholar
Samantaray, S. & Ghose, D. K. Prediction of S12-MKII Rainfall Simulator Experimental Runoff Data Sets (2022).
Masood, A. et al. Improving PM2.5 prediction in new Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm. Sci. Rep. 13 (1), 2105 (2023).
Google Scholar
Elbeltagi, A. et al. Forecasting actual evapotranspiration without climate data based on stacked integration of DNN and meta-heuristic models across China from 1958 to 2021. J. Environ. Manage. 345, 118697 (2023).
Google Scholar
Kumar, M. et al. The superiority of data-driven techniques for Estimation of daily pan evaporation. Atmosphere 12 (6), 701 (2021).
Google Scholar
Kushwaha, N. L. et al. Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: A case study from Northern India. Atmosphere 12 (12), 1654 (2021).
Google Scholar
Khosravi, K., Mao, L., Kisi, O., Yaseen, Z. M. & Shahid, S. Quantifying hourly suspended sediment load using data mining models: case study of a glacierized Andean catchment in Chile. J. Hydrol. 567, 165–179 (2018).
Google Scholar
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Research: Atmos. 106 (D7), 7183–7192 (2001).
Google Scholar
Vishwakarma, D. K. et al. Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions. Pure Appl. Geophys. 181, 719–747 (2024).
Google Scholar
Uynh, P. H., Nguyen, V. H. & Do, T. N. A coupling support vector machines with the feature learning of deep convolutional neural networks for gene expression data classification. In Modern Approaches for Intelligent Information and Database Systems (eds. Sieminski, A. et al.) 233–243 (Springer, 2018).
Díaz-Vico, D. et al. Deep support vector classification and regression. In From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science (eds. Ferrández Vicente, J.), vol. 11487, 33–43 (Springer, 2019).
Hahmad, J. et al. Determining speaker attributes from stress-affected speech in emergency situations with hybrid SVM-DNN architecture. Multimedia Tools Appl. 77, 4883–4907 (2018).
Google Scholar
Ma, T., Yu, Y., Wang, F., Zhang, Q. & Chen, X. A hybrid methodologies for intrusion detection based deep neural network with support vector machine and clustering technique. In Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering (eds Yen, N. & Hung, J.), vol. 422 (Springer, 2018).
Jo, G. R., Baek, B., Kim, Y. S. & Lim, D. H. Transfer learning based DNN-SVM hybrid model for breast cancer classification. J. Korea Soc. Comput. Inform. 28 (11), 1–11 (2023).
Elbeltagi, A. et al. Advanced stacked integration method for forecasting long-term drought severity: CNN with machine learning models. J. Hydrology: Reg. Stud. 53, 101759 (2024).
Prasunna, D. L. N. & Ashesh, K. Advancements in skin cancer diagnosis: A literature survey and hybrid approach employing SVM and DNN models with results analysis. In 2024 IEEE 6th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) 73–77 (IEEE, 2024).
Chen, J. L., Yang, H., Lv, M. Q., Xiao, Z. L. & Wu, S. J. Estimation of monthly pan evaporation using support vector machine in three Gorges reservoir area, China. Theoret. Appl. Climatol. 138 (1), 1095–1107 (2019).
Google Scholar
Lin, G. F., Lin, H. Y. & Wu, M. C. Development of a support-vector‐machine‐based model for daily pan evaporation Estimation. Hydrol. Process. 27 (22), 3115–3312 (2013).
Google Scholar
Roy, D. K. et al. Daily prediction and multi-step forward forecasting of reference evapotranspiration using LSTM and Bi-LSTM models. Agronomy 12 (3), 594 (2022).
Google Scholar
El-Kenawy, E. S. M. et al. Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. Environ. Sci. Pollut. Res. 1, 1–2 (2022).
