{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T21:40:44Z","timestamp":1774906844606,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Einstein Research Unit \u201cClimate and Water under Change\u201d from the Einstein Foundation Berlin and Berlin University Alliance","award":["ERU-2020-609"],"award-info":[{"award-number":["ERU-2020-609"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate drought forecasting is necessary for effective agricultural and water resource management and for early risk warning. Various machine learning models have been developed for drought forecasting. This work developed and tested a fusion-based ensemble model, namely, the stacking (ST) model, that integrates extreme gradient boosting (XGBoost), random forecast (RF), and light gradient boosting machine (LightGBM) for drought forecasting. Additionally, the ST model employs the SHapley Additive exPlanations (SHAP) algorithm to interpret the relationship between variables and forecasting results. Multi-source data that encompass meteorological, vegetation, anthropogenic, landcover, climate teleconnection patterns, and topological characteristics were incorporated in the proposed ST model. The ST model forecasts the one-month lead standardized precipitation evapotranspiration index (SPEI) at a 12 month scale. The proposed ST model was applied and tested in the German federal states of Brandenburg and Berlin. The results show that the ST model outperformed the reference persistence model, XGBboost, RF, and LightGBM, achieving an average coefficient of determination (R2) value of 0.845 in each month in 2018. The spatiotemporal Moran\u2019s I method indicates that the ST model captures non-stationarity in modeling the statistical association between predictors and the meteorological drought index and outperforms the other three models (i.e., XGBoost, RF, and LightGBM). Global sensitivity analysis indicates that the ST model is influenced by a combination of environmental variables, with the most sensitive being the preceding drought indices. The accuracy and versatility of the ST model indicate that this is a promising approach for forecasting drought and other environmental phenomena.<\/jats:p>","DOI":"10.3390\/rs16050828","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T07:56:02Z","timestamp":1709106962000},"page":"828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Novel Fusion-Based Methodology for Drought Forecasting"],"prefix":"10.3390","volume":"16","author":[{"given":"Huihui","family":"Zhang","sequence":"first","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, 12489 Berlin, Germany"}]},{"given":"Hugo A.","family":"Loaiciga","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California, Santa Barbara, CA 93106, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2232-8096","authenticated-orcid":false,"given":"Tobias","family":"Sauter","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, 12489 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1002\/2016RG000549","article-title":"Seasonal drought prediction: Advances, challenges, and future prospects","volume":"56","author":"Hao","year":"2018","journal-title":"Rev. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"108080","DOI":"10.1016\/j.asoc.2021.108080","article-title":"Artificial neural networks in drought prediction in the 21st century\u2014A scientometric analysis","volume":"114","author":"Dikshit","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129750","DOI":"10.1016\/j.jhydrol.2023.129750","article-title":"Quantifying changes and trends of NO3 concentrations and concentration-discharge relationships in a complex, heavily managed, drought-sensitive river system","volume":"622","author":"Liu","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1111\/gcb.16584","article-title":"Crop residue return sustains global soil ecological stoichiometry balance","volume":"29","author":"Liu","year":"2023","journal-title":"Glob. Chang. Biol."},{"key":"ref_5","first-page":"W01009","article-title":"On the probability of droughts: The compound renewal model","volume":"41","year":"2005","journal-title":"Water Resour. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10584-016-1798-7","article-title":"Natural hazards in Australia: Droughts","volume":"139","author":"Kiem","year":"2016","journal-title":"Clim. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/ngeo2646","article-title":"Drought in the Anthropocene","volume":"9","author":"Gleeson","year":"2016","journal-title":"Nat. Geosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.rse.2018.04.001","article-title":"A fusion-based methodology for meteorological drought estimation using remote sensing data","volume":"211","author":"Alizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112514","DOI":"10.1016\/j.rse.2021.112514","article-title":"Geographically and temporally weighted neural network for winter wheat yield prediction","volume":"262","author":"Feng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126948","DOI":"10.1016\/j.jhydrol.2021.126948","article-title":"Reassessing the frequency and severity of meteorological drought considering non-stationarity and copula-based bivariate probability","volume":"603","author":"Jehanzaib","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1613\/jair.4992","article-title":"A primer on neural network models for natural language processing","volume":"57","author":"Goldberg","year":"2016","journal-title":"J. Artif. Intell. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.iatssr.2019.11.008","article-title":"Deep learning-based image recognition for autonomous driving","volume":"43","author":"Fujiyoshi","year":"2019","journal-title":"IATSS Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1002\/2014RG000456","article-title":"Remote sensing of drought: Progress, challenges and opportunities","volume":"53","author":"AghaKouchak","year":"2015","journal-title":"Rev. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.agrformet.2015.10.011","article-title":"Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions","volume":"216","author":"Park","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s00704-015-1624-6","article-title":"Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities","volume":"127","author":"Kousari","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117242","DOI":"10.1016\/j.atmosenv.2019.117242","article-title":"Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: The ST-stacking model","volume":"223","author":"Feng","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114498","DOI":"10.1016\/j.eswa.2020.114498","article-title":"Interpretable vs. noninterpretable machine learning models for data-driven hydro-climatological process modeling","volume":"170","author":"Chakraborty","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.resconrec.2016.04.008","article-title":"Phosphorus flows in Berlin-Brandenburg, a regional flow analysis","volume":"112","author":"Theobald","year":"2016","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1002\/hyp.7210","article-title":"Simulation and analysis of runoff from a partly glaciated meso-scale catchment area in Patagonia using an artificial neural network","volume":"23","author":"Sauter","year":"2009","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6132","DOI":"10.1175\/2011JCLI4155.1","article-title":"Natural three-dimensional predictor domains for statistical precipitation downscaling","volume":"24","author":"Sauter","year":"2011","journal-title":"J. Clim."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1038\/d41586-020-01812-9","article-title":"Five ways to ensure that models serve society: A manifesto","volume":"582","author":"Saltelli","year":"2020","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.5194\/nhess-15-1381-2015","article-title":"Exploring the link between drought indicators and impacts","volume":"15","author":"Bachmair","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"014037","DOI":"10.1088\/1748-9326\/ad10d9","article-title":"Significant relationships between drought indicators and impacts for the 2018\u20132019 drought in Germany","volume":"19","author":"Shyrokaya","year":"2023","journal-title":"Environ. Res. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"128335","DOI":"10.1016\/j.jhydrol.2022.128335","article-title":"The imprint of hydroclimate, urbanization and catchment connectivity on the stable isotope dynamics of a large river in Berlin, Germany","volume":"613","author":"Kuhlemann","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1016\/j.ecolmodel.2009.04.038","article-title":"Impact of climate change on soil moisture dynamics in Brandenburg with a focus on nature conservation areas","volume":"220","author":"Holsten","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112781","DOI":"10.1016\/j.rse.2021.112781","article-title":"Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series","volume":"268","author":"Kowalski","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/(SICI)1097-0088(199604)16:4<361::AID-JOC53>3.0.CO;2-F","article-title":"Calculating regional climatic time series for temperature and precipitation: Methods and illustrations","volume":"16","author":"Jones","year":"1996","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.5194\/asr-10-99-2013","article-title":"Monitoring of climate change in Germany\u2013data, products and services of Germany's National Climate Data Centre","volume":"10","author":"Kaspar","year":"2013","journal-title":"Adv. Sci. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"135606","DOI":"10.1016\/j.scitotenv.2019.135606","article-title":"Evaluation of GRACE mascon solutions using in-situ geodetic data: The case of hydrologic-induced crust displacement in the Yangtze River Basin","volume":"707","author":"Wang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1002\/joc.7269","article-title":"Evaluation of daily precipitation analyses in E-OBS (v19. 0e) and ERA5 by comparison to regional high-resolution datasets in European regions","volume":"42","author":"Bandhauer","year":"2022","journal-title":"Int. J. Climatol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"158425","DOI":"10.1016\/j.scitotenv.2022.158425","article-title":"Hydrological drought evaluation using GRACE satellite-based drought index over the lake basins, East Africa","volume":"852","author":"Seka","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"126871","DOI":"10.1016\/j.jhydrol.2021.126871","article-title":"Use of a multiscalar GRACE-based standardized terrestrial water storage index for assessing global hydrological droughts","volume":"603","author":"Cui","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103645","DOI":"10.1016\/j.gloplacha.2021.103645","article-title":"Changes in the drought condition over northern East Asia and the connections with extreme temperature and precipitation indices","volume":"207","author":"Sun","year":"2021","journal-title":"Glob. Planet. Chang."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s00703-005-0122-2","article-title":"Assessment of climate extremes in the Eastern Mediterranean","volume":"89","author":"Kostopoulou","year":"2005","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.jhydrol.2011.08.049","article-title":"Trends of extreme precipitation and associated synoptic patterns over the southern Iberian Peninsula","volume":"409","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1175\/1520-0442(2003)016<3665:TIIODT>2.0.CO;2","article-title":"Trends in indices of daily temperature and precipitation extremes in Europe, 1946\u201399","volume":"16","author":"Tank","year":"2003","journal-title":"J. Clim."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"709","DOI":"10.5194\/hess-18-709-2014","article-title":"Variability of extreme precipitation over Europe and its relationships with teleconnection patterns","volume":"18","author":"Casanueva","year":"2014","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1023\/A:1005491526870","article-title":"CLIVAR\/GCOS\/WMO workshop on indices and indicators for climate extremes: Workshop summary","volume":"42","author":"Karl","year":"1999","journal-title":"Clim. Chang."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7","DOI":"10.5194\/asr-11-7-2014","article-title":"Prototype of a drought monitoring and forecasting system for the Tuscany region","volume":"11","author":"Magno","year":"2014","journal-title":"Adv. Sci. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.jhydrol.2015.01.070","article-title":"Probabilistic forecasting of drought class transitions in Sicily (Italy) using standardized precipitation index and North Atlantic oscillation index","volume":"526","author":"Bonaccorso","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8554","DOI":"10.1002\/2016GL069847","article-title":"Long-term predictability of soil moisture dynamics at the global scale: Persistence versus large-scale drivers","volume":"43","author":"Gudmundsson","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Abiy, A.Z., Melesse, A.M., and Abtew, W. (2019). Teleconnection of regional drought to ENSO, PDO, and AMO: Southern Florida and the Everglades. Atmosphere, 10.","DOI":"10.3390\/atmos10060295"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.1002\/joc.7290","article-title":"The role of teleconnection patterns in the variability and trends of growing season indices across Europe","volume":"42","author":"Craig","year":"2022","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"127290","DOI":"10.1016\/j.jhydrol.2021.127290","article-title":"Hydrological drought variability and its teleconnections with climate indices","volume":"605","author":"Abdelkader","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"300","DOI":"10.2134\/agronj1984.00021962007600020029x","article-title":"Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1","volume":"76","author":"Asrar","year":"1984","journal-title":"Agron. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106328","DOI":"10.1016\/j.catena.2022.106328","article-title":"Response of vegetation to drought and yield monitoring based on NDVI and SIF","volume":"219","author":"Ding","year":"2022","journal-title":"Catena"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0034-4257(02)00084-6","article-title":"An overview of MODIS Land data processing and product status","volume":"83","author":"Justice","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1029\/2018RG000608","article-title":"An overview of global leaf area index (LAI): Methods, products, validation, and applications","volume":"57","author":"Fang","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e2019RG000683","DOI":"10.1029\/2019RG000683","article-title":"Anthropogenic drought: Definition, challenges, and opportunities","volume":"59","author":"AghaKouchak","year":"2021","journal-title":"Rev. Geophys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1002\/rra.3518","article-title":"Changes in the flow regimes associated with climate change and human activities in the Yangtze River","volume":"35","author":"Cheng","year":"2019","journal-title":"River Res. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"114259","DOI":"10.1016\/j.envpol.2020.114259","article-title":"Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis","volume":"262","author":"Han","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"267","DOI":"10.5194\/esd-9-267-2018","article-title":"Global drought and severe drought-affected populations in 1.5 and 2\u2218C warmer worlds","volume":"9","author":"Liu","year":"2018","journal-title":"Earth Syst. Dyn."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"110584","DOI":"10.1016\/j.ecolind.2023.110584","article-title":"Drought monitoring based on temperature vegetation dryness index and its relationship with anthropogenic pressure in a subtropical humid watershed in China","volume":"154","author":"Yuan","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"889","DOI":"10.5194\/essd-13-889-2021","article-title":"An extended time-series (2000\u20132018) of global NPP-VIIRS-like nighttime light data Version V3) Harvard Dataverse","volume":"13","author":"Chen","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1175\/JCLI-D-12-00369.1","article-title":"Drought and deforestation: Has land cover change influenced recent precipitation extremes in the Amazon?","volume":"27","author":"Bagley","year":"2014","journal-title":"J. Clim."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1080\/01431161.2021.1881185","article-title":"Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation","volume":"42","author":"Kowe","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","unstructured":"Turner, M.G., Gardner, R.H., O\u2019neill, R.V., and O\u2019Neill, R.V. (2001). Landscape Ecology in Theory and Practice, Springer."},{"key":"ref_61","unstructured":"Harris, D., and Harris, S.L. (2010). Digital Design and Computer Architecture, Morgan Kaufmann."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1175\/2009JCLI2909.1","article-title":"A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index","volume":"23","year":"2010","journal-title":"J. Clim."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111979","DOI":"10.1016\/j.jenvman.2021.111979","article-title":"An improved SPEI drought forecasting approach using the long short-term memory neural network","volume":"283","author":"Dikshit","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.5194\/nhess-21-1685-2021","article-title":"Changes in drought features at the European level over the last 120 years","volume":"21","author":"Ionita","year":"2021","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"054025","DOI":"10.1088\/1748-9326\/aabf20","article-title":"Drought-sensitivity of fine dust in the US Southwest: Implications for air quality and public health under future climate change","volume":"13","author":"Achakulwisut","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_66","unstructured":"Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, Academic Press."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"107028","DOI":"10.1016\/j.agwat.2021.107028","article-title":"Agricultural drought prediction in China based on drought propagation and large-scale drivers","volume":"255","author":"Zhang","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1080\/02626667.2020.1784901","article-title":"Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models","volume":"65","author":"Zhang","year":"2020","journal-title":"Hydrol. Sci. J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Herrera, F., Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F., Charte, F., Rivera, A.J., and del Jesus, M.J. (2016). Multilabel Classification, Springer.","DOI":"10.1007\/978-3-319-41111-8"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"106961","DOI":"10.1016\/j.atmosres.2023.106961","article-title":"Seasonal forecast of winter precipitation over China using machine learning models","volume":"294","author":"Qian","year":"2023","journal-title":"Atmos. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"165509","DOI":"10.1016\/j.scitotenv.2023.165509","article-title":"Explainable machine learning for the prediction and assessment of complex drought impacts","volume":"898","author":"Zhang","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_73","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_75","unstructured":"Michailidis, M. (2023, April 15). StackNet, StackNet Meta Modelling Framework. Available online: https:\/\/github.com\/kaz-Anova\/StackNet."},{"key":"ref_76","first-page":"200100","article-title":"Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling","volume":"15","author":"Farhangi","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"103562","DOI":"10.1016\/j.advwatres.2020.103562","article-title":"Prediction of droughts over Pakistan using machine learning algorithms","volume":"139","author":"Khan","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"149797","DOI":"10.1016\/j.scitotenv.2021.149797","article-title":"Interpretable and explainable AI (XAI) model for spatial drought prediction","volume":"801","author":"Dikshit","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Cheng, M., Zhong, L., Ma, Y., Wang, X., Li, P., Wang, Z., and Qi, Y. (2023). A new drought monitoring index on the Tibetan Plateau based on multisource data and machine learning methods. Remote Sens., 15.","DOI":"10.3390\/rs15020512"},{"key":"ref_82","first-page":"307","article-title":"A value for n-person games","volume":"2","author":"Shapley","year":"1953","journal-title":"Contrib. Theory Games"},{"key":"ref_83","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_84","unstructured":"Molnar, C. (2020). Interpretable Machine Learning, Lulu Press."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"163004","DOI":"10.1016\/j.scitotenv.2023.163004","article-title":"Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model","volume":"879","author":"Abdollahi","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.solener.2020.12.045","article-title":"Use of physics to improve solar forecast: Physics-informed persistence models for simultaneously forecasting GHI, DNI, and DHI","volume":"215","author":"Liu","year":"2021","journal-title":"Sol. Energy"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1080\/10095020.2019.1643609","article-title":"Measuring spatio-temporal autocorrelation in time series data of collective human mobility","volume":"22","author":"Gao","year":"2019","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_89","unstructured":"Rogerson, P.A. (2021). Spatial Statistical Methods for Geography, Sage Publishing."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"e2020GL087360","DOI":"10.1029\/2020GL087360","article-title":"Current models underestimate future irrigated areas","volume":"47","author":"Puy","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_91","first-page":"377","article-title":"Sensitivity analysis as an ingredient of modeling","volume":"15","author":"Saltelli","year":"2000","journal-title":"Stat. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"105327","DOI":"10.1016\/j.envsoft.2022.105327","article-title":"A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions","volume":"149","author":"Prodhan","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s11069-019-03665-6","article-title":"Drought forecasting through statistical models using standardised precipitation index: A systematic review and meta-regression analysis","volume":"97","author":"Anshuka","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.jhydrol.2016.05.042","article-title":"Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts","volume":"539","author":"Maity","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1080\/02693799608902100","article-title":"The geography of parameter space: An investigation of spatial non-stationarity","volume":"10","author":"Charlton","year":"1996","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"10441","DOI":"10.1007\/s12652-022-03701-7","article-title":"A novel intelligent deep learning predictive model for meteorological drought forecasting","volume":"14","author":"Mehr","year":"2023","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"e2022WR033847","DOI":"10.1029\/2022WR033847","article-title":"A machine learning framework for predicting and understanding the Canadian drought monitor","volume":"59","author":"Mardian","year":"2023","journal-title":"Water Resour. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/828\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:06:09Z","timestamp":1760105169000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/828"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":97,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050828"],"URL":"https:\/\/doi.org\/10.3390\/rs16050828","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]}}}