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Research Article

Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning

, , , , , & show all
Pages 1984-2008 | Received 18 Oct 2022, Accepted 13 Jul 2023, Published online: 12 Sep 2023

References

  • Abatzoglou, J.T., et al., 2018. Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Scientific Data, 5, 170191. doi:10.1038/sdata.2017.191
  • Abdi, H., 2003. Partial least square regression (PLS regression). Encyclopedia for research methods for the social sciences. Nature Neuroscience, 6 (4), 792–795. doi:10.1038/nn0803-792
  • Ahmed, A.M., et al., 2022. Kernel ridge regression hybrid method for wheat yield prediction with satellite-derived predictors. Remote Sensing, 14 (5), 1136. doi:10.3390/rs14051136
  • Alawsi, M.A., et al., 2022. Drought forecasting: a review and assessment of the hybrid techniques and data pre-processing. Hydrology, 9 (7), 115. doi:10.3390/hydrology9070115
  • Ali, M., et al., 2020. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. Journal of Hydrology, 584, 124647. doi:10.1016/j.jhydrol.2020.124647
  • Alizadeh, M.R. and Nikoo, M.R., 2018. A fusion-based methodology for meteorological drought estimation using remote sensing data. Remote Sensing of Environment, 211, 229–247. doi:10.1016/j.rse.2018.04.001
  • An, S., Liu, W., and Venkatesh, S., 2007. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition, 40 (8), 2154–2162. doi:10.1016/j.patcog.2006.12.015
  • Arvor, D., et al., 2017. Monitoring rainfall patterns in the southern Amazon with PERSIANN-CDR data: long-term characteristics and trends. Remote Sensing, 9 (9), 889. doi:10.3390/rs9090889
  • Ashfaq, N., Nawaz, Z., and Ilyas, M., 2021. A comparative study of different machine learning regressors for stock market prediction. arXiv preprint arXiv, 2104, 07469.
  • Ashouri, H., et al., 2015. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96 (1), 69–83. doi:10.1175/BAMS-D-13-00068.1
  • Ayugi, B., et al., 2020. Quantile mapping bias correction on rossby centre regional climate models for precipitation analysis over Kenya, East Africa. Water, 12 (3), 801. doi:10.3390/w12030801
  • Bahraini, M.S., Rad, A.B., and Bozorg, M., 2019. SLAM in dynamic environments: a deep learning approach for moving object tracking using ML-RANSAC algorithm. Sensors, 19 (17), 3699. doi:10.3390/s19173699
  • Belayneh, A., et al., 2020. Evaluation of satellite precipitation products using HEC-HMS model. Modeling Earth Systems and Environment, 6 (4), 2015–2032. doi:10.1007/s40808-020-00792-z
  • Boser, B.E., Guyon, I.M., and Vapnik, V.N., 1992. A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, 144–152.
  • Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324
  • Calheiros, R.V. and Zawadzki, I., 1987. Reflectivity-rain rate relationships for radar hydrology in Brazil. Journal of Applied Meteorology and Climatology, 26 (1), 118–132. doi:10.1175/1520-0450(1987)026<0118:RRRRFR>2.0.CO;2
  • Chen, T. and Guestrin, C., 2016. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785–794.
  • Chivers, B.D., et al., 2020. Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach. Journal of Hydrology, 588, 125126. doi:10.1016/j.jhydrol.2020.125126
  • Choubin, B., et al., 2016. Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61 (6), 1001–1009. doi:10.1080/02626667.2014.966721
  • Crochemore, L., Ramos, M.H., and Pappenberger, F., 2016. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Hydrology and Earth System Sciences, 20 (9), 3601–3618. doi:10.5194/hess-20-3601-2016
  • Darji, M.P., Dabhi, V.K., and Prajapati, H.B., 2015, March. Rainfall forecasting using neural network: a survey. In: 2015 international conference on advances in computer engineering and applications, IEEE, 706–713.
  • Diez-Sierra, J. and Del Jesus, M., 2020. Long-term rainfall prediction using atmospheric synoptic patterns in semi-arid climates with statistical and machine learning methods. Journal of Hydrology, 586, 124789. doi:10.1016/j.jhydrol.2020.124789
  • Dikshit, A. and Pradhan, B., 2021. Explainable AI in drought forecasting. Machine Learning with Applications, 6, 100192. doi:10.1016/j.mlwa.2021.100192
  • Dong, Q., et al., 2021. Interpenetrating interfaces for efficient perovskite solar cells with high operational stability and mechanical robustness. Nature Communications, 12 (1), 1–9. doi:10.1038/s41467-021-21292-3
  • Ekong, E.E., et al., 2019. Performance comparison of ANN training algorithms for hysteresis determination in LTE networks. In: Journal of Physics: Conference Series, (Vol. 1378, No. 4) IOP Publishing, 042094.
  • Farajzadeh, J. and Alizadeh, F., 2018. A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model. Journal of Hydroinformatics, 20 (1), 246–262. doi:10.2166/hydro.2017.013
  • Fooladi, M., et al., 2021. Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: resilience, vulnerability, and frequency analysis. Journal of Environmental Management, 297, 113283. doi:10.1016/j.jenvman.2021.113283
  • Fooladi, M., et al., 2023. Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran. Expert Systems with Applications, 221, 119788. doi:10.1016/j.eswa.2023.119788
  • Freund, Y. and Schapire, R.E., 1996. Experiments with a new boosting algorithm. In: icml. Vol. 96. 148–156.
  • Fu, T., et al., 2021. A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland. Journal of Hydrology, 603, 126881. doi:10.1016/j.jhydrol.2021.126881
  • Funk, C., et al., 2015. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2 (1), 1–21. doi:10.1038/sdata.2015.66
  • Gado, T.A., Hsu, K., and Sorooshian, S., 2017. Rainfall frequency analysis for ungauged sites using satellite precipitation products. Journal of Hydrology, 554, 646–655. doi:10.1016/j.jhydrol.2017.09.043
  • Ghorbanpour, A.K., et al., 2021. Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin. Journal of Hydrology, 596, 126055. doi:10.1016/j.jhydrol.2021.126055
  • Gibson, P.B., et al., 2021. Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Communications Earth & Environment, 2 (1), 159. doi:10.1038/s43247-021-00225-4
  • Gilewski, P. and Nawalany, M., 2018. Inter-comparison of rain-gauge, radar, and satellite (IMERG GPM) precipitation estimates performance for rainfall-runoff modeling in a mountainous catchment in Poland. Water, 10 (11), 1665. doi:10.3390/w10111665
  • Gouravaraju, S., et al., 2023. A Bayesian regularization-backpropagation neural network model for peeling computations. The Journal of Adhesion, 99 (1), 92–115. doi:10.1080/00218464.2021.2001335
  • Gu, J., et al., 2022. A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China. Water, 14 (3), 492. doi:10.3390/w14030492
  • Hammad, M., et al., 2021. Rainfall forecasting in upper Indus basin using various artificial intelligence techniques. Stochastic Environmental Research and Risk Assessment, 35 (11), 2213–2235. doi:10.1007/s00477-021-02013-0
  • Han, H. and Morrison, R.R., 2021. Data-driven approaches for runoff prediction using distributed data. Stochastic Environmental Research and Risk Assessment, 1–19.
  • He, M. and Gautam, M., 2016. Variability and trends in precipitation, temperature and drought indices in the State of California. Hydrology, 3 (2), 14. doi:10.3390/hydrology3020014
  • He, X., et al., 2020. A hybrid model based on variational mode decomposition and gradient boosting regression tree for monthly runoff forecasting. Water Resources Management, 34 (2), 865–884. doi:10.1007/s11269-020-02483-x
  • Hersbach, H., et al., 2018. ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). doi:10.24381/cds.adbb2d47
  • Hotelling, H., 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24 (6), 417. doi:10.1037/h0071325
  • Hu, T., Wu, F., and Zhang, X., 2007. Rainfall–runoff modeling using principal component analysis and neural network. Hydrology Research, 38 (3), 235–248. doi:10.2166/nh.2007.010
  • Hung, N.Q., et al., 2009. An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 13 (8), 1413–1425. doi:10.5194/hess-13-1413-2009
  • Jafari, S.M., et al., 2023. Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques. Environmental Science and Pollution Research, 30 (14), 42087–42107.
  • Jakob Themeßl, M., Gobiet, A., and Leuprecht, A., 2011. Empirical‐statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology, 31 (10), 1530–1544. doi:10.1002/joc.2168
  • Jiang, S., et al., 2012. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. Journal of Hydrology, 452, 213–225. doi:10.1016/j.jhydrol.2012.05.055
  • Katiraie-Boroujerdy, P.S., et al., 2020. Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran. Remote Sensing, 12 (13), 2102. doi:10.3390/rs12132102
  • Khan, M.I. and Maity, R., 2020. Hybrid deep learning approach for multi-step-ahead daily rainfall prediction using GCM simulations. IEEE Access, 8, 52774–52784. doi:10.1109/ACCESS.2020.2980977
  • Kotsiantis, S.B., Kanellopoulos, D., and Pintelas, P.E., 2006. Data pre-processing for supervised leaning. International Journal of Computer Science, 1 (2), 111–117.
  • Krishankumar, R., et al., 2021. An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information. Complex & Intelligent Systems, 7 (5), 2281–2298. doi:10.1007/s40747-021-00387-w
  • Kumar, A., et al., 2020. Convcast: an embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data. Plos one, 15 (3), e0230114. doi:10.1371/journal.pone.0230114
  • Kumar, D., et al., 2019. Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal, 64 (6), 690–700. doi:10.1080/02626667.2019.1595624
  • Kumar, M., et al., 2021. Measuring precipitation in Eastern Himalaya: ground validation of eleven satellite, model and gauge interpolated gridded products. Journal of Hydrology, 599, 126252. doi:10.1016/j.jhydrol.2021.126252
  • Lazri, M., et al., 2020. Improving satellite rainfall estimation from MSG data in Northern Algeria by using a multi-classifier model based on machine learning. Journal of Hydrology, 584, 124705. doi:10.1016/j.jhydrol.2020.124705
  • Le, X.H., et al., 2020. Application of convolutional neural network for spatiotemporal bias correction of daily satellite-based precipitation. Remote Sensing, 12 (17), 2731. doi:10.3390/rs12172731
  • Li, J., et al., 2012. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Advances in Computer Science and Information Engineering: volume 2. Berlin Heidelberg: Springer, 553–558.
  • Li, L., et al., 2020. Ensemble-based deep learning for estimating PM2. 5 over California with multisource big data including wildfire smoke. Environment International, 145, 106143. doi:10.1016/j.envint.2020.106143
  • Li, X., Sha, J., and Wang, Z.L., 2019. Comparison of daily streamflow forecasts using extreme learning machines and the random forest method. Hydrological Sciences Journal, 64 (15), 1857–1866. doi:10.1080/02626667.2019.1680846
  • Malayeri, A.K., Saghafian, B., and Raziei, T., 2021. Performance evaluation of ERA5 precipitation estimates across Iran. Arabian Journal of Geosciences, 14 (23), 1–18. doi:10.1007/s12517-021-09079-8
  • Malekmohamadi, I., et al., 2011. Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction. Ocean Engineering, 38 (2–3), 487–497. doi:10.1016/j.oceaneng.2010.11.020
  • Mann, M.E. and Gleick, P.H., 2015. Climate change and California drought in the 21st century. Proceedings of the National Academy of Sciences, 112(13), 3858–3859.
  • Mardani, A., et al., 2015. Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic research-Ekonomska istraživanja, 28 (1), 516–571. doi:10.1080/1331677X.2015.1075139
  • Markovics, D. and Mayer, M.J., 2022. Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction. Renewable and Sustainable Energy Reviews, 161, 112364. doi:10.1016/j.rser.2022.112364
  • Menne, M.J., et al., 2012a. Global historical climatology network-daily (GHCN-Daily), Version 3. NOAA National Climatic Data Center, 10, V5D21VHZ.
  • Menne, M.J., et al., 2012b. An overview of the global historical climatology network-daily database. Journal of Atmospheric and Oceanic Technology, 29 (7), 897–910. doi:10.1175/JTECH-D-11-00103.1
  • Meydani, A., et al., 2022. Daily reservoir inflow forecasting using weather forecast downscaling and rainfall-runoff modeling: application to Urmia Lake basin, Iran. Journal of Hydrology: Regional Studies, 44, 101228.
  • Mishra, A.R., et al., 2020. Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability, 12 (10), 4155. doi:10.3390/su12104155
  • Mishra, N., et al., 2018. Development and analysis of artificial neural network models for rainfall prediction by using time-series data. International Journal of Intelligent Systems & Applications, 10 (1), 16–23. doi:10.5815/ijisa.2018.01.03
  • Moazami, S. and Najafi, M.R., 2021. A comprehensive evaluation of GPM-IMERG V06 and MRMS with hourly ground-based precipitation observations across Canada. Journal of Hydrology, 594, 125929. doi:10.1016/j.jhydrol.2020.125929
  • Modaresi, F., Araghinejad, S., and Ebrahimi, K., 2018. Selected model fusion: an approach for improving the accuracy of monthly streamflow forecasting. Journal of Hydroinformatics, 20 (4), 917–933. doi:10.2166/hydro.2018.098
  • Moges, D.M., Kmoch, A., and Uuemaa, E., 2022. Application of satellite and re-analysis precipitation products for hydrological modeling in the data-scarce Porijõgi catchment, Estonia. Journal of Hydrology: Regional Studies, 41, 101070.
  • Mosavi, A., Ozturk, P., and Chau, K.W., 2018. Flood prediction using machine learning models: literature review. Water, 10 (11), 1536. doi:10.3390/w10111536
  • Mulualem, G.M. and Liou, Y.A., 2020. Application of artificial neural networks in forecasting a standardized precipitation evapotranspiration index for the Upper Blue Nile basin. Water, 12 (3), 643. doi:10.3390/w12030643
  • Munir, M., et al., 2019. Fusead: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors, 19 (11), 2451. doi:10.3390/s19112451
  • Narejo, S., et al., 2021. Multi-step rainfall forecasting using deep learning approach. Peer Journal Computer Science, 7, e514. doi:10.7717/peerj-cs.514
  • Nastos, P.T., et al., 2014. Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmospheric Research, 144, 141–150. doi:10.1016/j.atmosres.2013.11.013
  • Nematollahi, B., et al., 2022a. A stochastic conflict resolution optimization model for flood management in detention basins: application of fuzzy graph model. Water, 14 (5), 774. doi:10.3390/w14050774
  • Nematollahi, B., et al., 2022b. A multi-criteria decision-making optimization model for flood management in reservoirs. Water Resources Management, 36 (13), 4933–4949.
  • Ni, L., et al., 2020. Streamflow and rainfall forecasting by two long short-term memory-based models. Journal of Hydrology, 583, 124296. doi:10.1016/j.jhydrol.2019.124296
  • Nourani, V., et al., 2019. Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus. Theoretical and Applied Climatology, 138 (3), 1419–1434. doi:10.1007/s00704-019-02904-x
  • Osman, A.I.A., et al., 2021. Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12 (2), 1545–1556. doi:10.1016/j.asej.2020.11.011
  • Pathak, T.B., et al., 2018. Climate change trends and impacts on California agriculture: a detailed review. Agronomy, 8 (3), 25. doi:10.3390/agronomy8030025
  • Pearson, K., 1901. Principal components analysis. The London, Edinburgh and Dublin Philosophical Magazine and Journal of Science, 6 (2), 559. doi:10.1080/14786440109462720
  • Pfeifroth, U., Mueller, R., and Ahrens, B., 2013. Evaluation of satellite-based and re-analysis precipitation data in the tropical Pacific. Journal of Applied Meteorology and Climatology, 52 (3), 634–644. doi:10.1175/JAMC-D-12-049.1
  • Pham, B.T., et al., 2020. Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845. doi:10.1016/j.atmosres.2020.104845
  • Prodhan, F.A., et al., 2021. Deep learning for monitoring agricultural drought in south asia using remote sensing data. Remote Sensing, 13 (9), 1715. doi:10.3390/rs13091715
  • Qasem, S.N., et al., 2019. Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13 (1), 177–187. doi:10.1080/19942060.2018.1564702
  • Rahman, A.U., et al., 2022. Rainfall prediction system using machine learning fusion for smart cities. Sensors, 22 (9), 3504. doi:10.3390/s22093504
  • Rodríguez, R., et al., 2021. Water-quality data imputation with a high percentage of missing values: a machine learning approach. Sustainability, 13 (11), 6318. doi:10.3390/su13116318
  • Sadeghi, M., et al., 2021. Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: case study of major floods across Iran (Spring 2019). Journal of Hydrology, 600, 126569. doi:10.1016/j.jhydrol.2021.126569
  • Salaeh, N., et al., 2022. Long-short term memory technique for monthly rainfall prediction in Thale Sap Songkhla River Basin, Thailand. Symmetry, 14 (8), 1599. doi:10.3390/sym14081599
  • Scheuerer, M., et al., 2020. Using artificial neural networks for generating probabilistic subseasonal precipitation forecasts over California. Monthly Weather Review, 148 (8), 3489–3506. doi:10.1175/MWR-D-20-0096.1
  • Shen, R., et al., 2019. Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 79, 48–57. doi:10.1016/j.jag.2019.03.006
  • Shrestha, N., et al., 2021. Effects of drought on groundwater-fed lake areas in the Nebraska Sand Hills. Journal of Hydrology: Regional Studies, 36, 100877.
  • Sidhu, R.K., Kumar, R., and Rana, P.S., 2020. Machine learning based crop water demand forecasting using minimum climatological data. Multimedia Tools and Applications, 79 (19), 13109–13124. doi:10.1007/s11042-019-08533-w
  • Silverman, D. and Dracup, J.A., 2000. Artificial neural networks and long-range precipitation prediction in California. Journal of Applied Meteorology, 39 (1), 57–66. doi:10.1175/1520-0450(2000)039<0057:ANNALR>2.0.CO;2
  • Singh, A., et al., 2020. Estimation of soil moisture applying modified dubois model to Sentinel-1; a regional study from central India. Remote Sensing, 12 (14), 2266. doi:10.3390/rs12142266
  • Song, Z., et al., 2022. Regionalization of hydrological model parameters using gradient boosting machine. Hydrology and Earth System Sciences, 26 (2), 505–524. doi:10.5194/hess-26-505-2022
  • Sorjamaa, A., et al., 2007. Methodology for long-term prediction of time series. Neurocomputing, 70 (16–18), 2861–2869. doi:10.1016/j.neucom.2006.06.015
  • Su, J., et al., 2021. How reliable are the satellite-based precipitation estimations in guiding hydrological modelling in South China? Journal of Hydrology, 602, 126705. doi:10.1016/j.jhydrol.2021.126705
  • Sulugodu, B. and Deka, P.C., 2019. Evaluating the performance of CHIRPS satellite rainfall data for streamflow forecasting. Water Resources Management, 33 (11), 3913–3927. doi:10.1007/s11269-019-02340-6
  • Sumi, S.M., Zaman, M.F., and Hirose, H., 2012. A rainfall forecasting method using machine learning models and its application to the Fukuoka city case. International Journal of Applied Mathematics and Computer Science, 22 (4), 841–854. doi:10.2478/v10006-012-0062-1
  • Switanek, M.B., et al., 2017. Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrology and Earth System Sciences, 21 (6), 2649–2666. doi:10.5194/hess-21-2649-2017
  • Tabari, H., 2020. Climate change impact on flood and extreme precipitation increases with water availability. Scientific Reports, 10 (1), 1–10. doi:10.1038/s41598-019-56847-4
  • Tao, Y., et al., 2016. A deep neural network modeling framework to reduce bias in satellite precipitation products. Journal of Hydrometeorology, 17 (3), 931–945. doi:10.1175/JHM-D-15-0075.1
  • Taravatrooy, N., et al., 2018. A hybrid clustering-fusion methodology for land subsidence estimation. Natural Hazards, 94 (2), 905–926. doi:10.1007/s11069-018-3431-8
  • Tharun, V.P., Prakash, R., and Devi, S.R., 2018. Prediction of rainfall using data mining techniques. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 1507–1512.
  • Tramblay, Y., et al., 2016. Evaluation of satellite-based rainfall products for hydrological modelling in Morocco. Hydrological Sciences Journal, 61 (14), 2509–2519. doi:10.1080/02626667.2016.1154149
  • Wei, S., et al., 2013. A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrological Sciences Journal, 58 (2), 374–389. doi:10.1080/02626667.2012.754102
  • Wei, X., et al., 2021. Multi-source hierarchical data fusion for high-resolution AOD mapping in a forest fire event. International Journal of Applied Earth Observation and Geoinformation, 102, 102366. doi:10.1016/j.jag.2021.102366
  • Worland, S.C., Farmer, W.H., and Kiang, J.E., 2018. Improving predictions of hydrological low-flow indices in ungaged basins using machine learning. Environmental Modelling and Software, 101, 169–182. doi:10.1016/j.envsoft.2017.12.021
  • Wu, C.L., Chau, K.W., and Fan, C., 2010. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology, 389 (1–2), 146–167. doi:10.1016/j.jhydrol.2010.05.040
  • Wu, J., et al., 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17 (1), 26–40.
  • Xiang, Y., et al., 2018. A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883. doi:10.1016/j.asoc.2018.09.018
  • Xiao, C., et al., 2019. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment, 233, 111358. doi:10.1016/j.rse.2019.111358
  • Xu, S., et al., 2021. Spatial downscaling of land surface temperature based on a multi-factor geographically weighted machine learning model. Remote Sensing, 13 (6), 1186. doi:10.3390/rs13061186
  • Yan, B., et al., 2013. Impacts of land use change on watershed streamflow and sediment yield: an assessment using hydrologic modelling and partial least squares regression. Journal of Hydrology, 484, 26–37. doi:10.1016/j.jhydrol.2013.01.008
  • Yang, Y. and Yang, Y., 2020. Hybrid prediction method for wind speed combining ensemble empirical mode decomposition and Bayesian ridge regression. IEEE Access, 8, 71206–71218. doi:10.1109/ACCESS.2020.2984020
  • Yazdandoost, F., Razavi, H., and Izadi, A., 2022. Optimization of agricultural patterns based on virtual water considerations through integrated water resources management modeling. International Journal of River Basin Management, 20 (2), 255–263. doi:10.1080/15715124.2021.1879093
  • Yeditha, P.K., et al., 2020. Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30 (6), 063115. doi:10.1063/5.0008195
  • Yu, C., et al., 2021. Performance evaluation of IMERG precipitation products during typhoon Lekima (2019). Journal of Hydrology, 597, 126307. doi:10.1016/j.jhydrol.2021.126307
  • Yu, X., et al., 2020. Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting. Journal of Hydrology, 582, 124293. doi:10.1016/j.jhydrol.2019.124293
  • Zavadskas, E.K., Kaklauskas, A., and Sarka, V., 1994. The new method of multi-criteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1 (3), 131–139.
  • Zhang, X., et al., 2020. Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha. IEEE Access, 8, 30223–30233. doi:10.1109/ACCESS.2020.2972435
  • Zhou, Z., et al., 2021. A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors. Hydrological Processes, 35 (11), e14424. doi:10.1002/hyp.14424
  • Zou, H. and Hastie, T., 2005. Regularization and variable selection via the elasticnet. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 67 (2), 301–320. doi:10.1111/j.1467-9868.2005.00503.x

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