59 citations as recorded by crossref.

1. Implementation of XGBoost Models for Predicting CO2 Emission and Specific Tractor Fuel Consumption N. Balać et al. 10.3390/agriculture15111209
2. A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation T. Abekoon et al. 10.1016/j.atech.2025.100879
3. Spatiotemporal changes of vegetation in the northern foothills of Qinling Mountains based on kNDVI considering climate time-lag effects and human activities L. Chen et al. 10.1016/j.envres.2025.120959
4. AI models uncover factors influencing scorpionism in Northern Brazil T. Moura et al. 10.1016/j.toxicon.2025.108342
5. Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model X. Meng et al. 10.1080/15481603.2024.2367806
6. Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features R. Scheda & S. Diciotti 10.3390/app12136681
7. An agricultural drought early warning threshold model with considering copula combined with diminishing marginal benefit theory: A case study in the Yellow River basin T. Qian et al. 10.1016/j.agwat.2025.109582
8. Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty Q. Rong et al. 10.1016/j.jenvman.2022.114432
9. Soil physicochemical properties explain land use/cover histories in the last sixty years in China H. Chen et al. 10.1016/j.geoderma.2024.116908
10. Vapor pressure deficit and temperature variability drive future changes to carbon sink stability in China’s terrestrial ecosystems Z. Zhou et al. 10.3389/ffgc.2024.1518578
11. Towards a News Recommendation System to increase Reader Engagement through Newsletter Content Personalization E. Fernandes et al. 10.1016/j.procs.2024.06.165
12. Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data S. Biass et al. 10.5194/nhess-22-2829-2022
13. The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters M. Kruk et al. 10.1016/j.ecoinf.2021.101462
14. Assessing drought risk of grassland ecosystem in Hulunbuir, China J. Han et al. 10.1016/j.ecolind.2025.113522
15. Insights from land sparing and land sharing frameworks for land productivity degradation governance in the Yangtze River Delta urban agglomeration, China J. Qian et al. 10.1002/ldr.5219
16. Research and application of XGBoost in imbalanced data P. Zhang et al. 10.1177/15501329221106935
17. Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP W. Li et al. 10.3390/w17111619
18. Surgery duration: Optimized prediction and causality analysis O. Babayoff et al. 10.1371/journal.pone.0273831
19. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers G. Zhang et al. 10.1186/s40001-024-01756-0
20. Interpretable machine learning to forecast hypoxia in a lagoon D. Politikos et al. 10.1016/j.ecoinf.2021.101480
21. Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer S. Kumar & A. Das 10.1016/j.compbiolchem.2023.107867
22. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models O. Babayoff et al. 10.1007/s10916-022-01902-3
23. Modelling of fatty acids signatures predicts macroalgal carbon in marine sediments . Erlania et al. 10.1016/j.ecolind.2024.111715
24. Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model J. Peng et al. 10.3390/rs16081361
25. GRADIENT-BOOSTED CAUSAL INFERENCE FRAMEWORK FOR POLICY RECOMMENDATION IN SMART GOVERNANCE SYSTEMS P. Khairnar 10.29121/shodhkosh.v4.i2.2023.5543
26. Global map of a comprehensive drought/flood index and analysis of controlling environmental factors J. Pang & H. Zhang 10.1007/s11069-022-05673-5
27. A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods E. Fernandes et al. 10.1111/ijcs.13030
28. Partial Order Ranking of the Key Drivers of Grassland Conversion in the Urban–Grassland Interface: A Case Study of the Hohhot–Baotou–Ordos Region X. Li et al. 10.3390/app15115906
29. Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning T. Bai et al. 10.1016/j.jhydrol.2024.130747
30. Local climate zone framework: seasonal dynamics of surface urban heat island and its influencing factors in three Chinese urban agglomerations H. Deng et al. 10.1080/15481603.2025.2490317
31. Evaluating the seasonal impact of urbanisation on surface air temperature in Beijing over three decades: 1990s, 2010s, and 2030s L. Vitanova et al. 10.1088/2515-7620/adc908
32. Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects M. Ma et al. 10.1016/j.scitotenv.2022.160527
33. Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions J. Delaney & D. Larson 10.1111/cobi.14203
34. Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting M. Atiea et al. 10.1155/er/8022398
35. WBGT prediction with high spatial resolution using actual measurement data and data acquired using infrared sensors mounted on UAVs H. Niwa & R. Manabe 10.1016/j.scs.2024.105470
36. MODIS-Based Spatiotemporal Inversion and Driving-Factor Analysis of Cloud-Free Vegetation Cover in Xinjiang from 2000 to 2024 H. Yang et al. 10.3390/s25082394
37. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al. 10.1016/j.jhydrol.2022.127431
38. Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence S. Silva et al. 10.1029/2021MS002881
39. Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms S. Garini et al. 10.1016/j.mex.2024.103127
40. Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning F. Shi et al. 10.1021/acs.iecr.2c03064
41. Influential factors for the emission inspection results of urban in-use vehicles: From an ensemble learning perspective Q. Zhimei et al. 10.1080/10962247.2022.2035851
42. Biological response of Rhopalosiphum padi and Sipha flava (Hemiptera: Aphididae) changes over generations A. Auad et al. 10.1515/flaent-2024-0088
43. Attribution of vegetation dynamics using an interpretable machine learning model in the Haihe River Basin, China Q. Wang et al. 10.1016/j.ecofro.2025.05.022
44. Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. 10.5194/tc-16-4637-2022
45. Polarization of dryland vegetation response to spring flood S. Zhang et al. 10.1016/j.jenvman.2025.126175
46. The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin M. Ma et al. 10.1016/j.jhydrol.2023.129733
47. A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia C. Ojeda Riaños et al. 10.1016/j.jenvman.2024.122655
48. Modeling the Subpixel Land-Use Dynamics and Its Influence on Urban Heat Islands: Impacts of Factors and Scale, and Population Exposure Risk X. Liang et al. 10.1016/j.scs.2024.105417
49. Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020 G. Li et al. 10.3390/f15020231
50. Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means A. Soubeiga et al. 10.1016/j.eswa.2024.125369
51. Quantifying soil wind erosion attribution in Inner Mongolia’s desert grassland H. Jiang et al. 10.1038/s41598-025-98339-8
52. An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia S. Alqadhi et al. 10.3389/fevo.2023.1189184
53. Climate change enhances greening while human activities accelerate degradation in northern China's grasslands F. Cao et al. 10.1016/j.scitotenv.2025.178570
54. Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings J. Huang et al. 10.3390/buildings14092623
55. How does the ambient environment respond to the industrial heat island effects? An innovative and comprehensive methodological paradigm for quantifying the varied cooling effects of different landscapes J. Gao et al. 10.1080/15481603.2022.2127463
56. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening Y. Yan et al. 10.1038/s41559-024-02372-1
57. Predicting the Ecological Quality of Rivers: A Machine Learning Approach and a What-if Scenarios Tool D. Politikos et al. 10.1007/s10666-024-09980-y
58. Integration of shapley additive explanations with random forest model for quantitative precipitation estimation of mesoscale convective systems Z. He et al. 10.3389/fenvs.2022.1057081
59. Optimising oxygen mass transfer in venturi flumes: a data-driven through bagging and boosting perspectives D. Panwar & N. Tiwari 10.1007/s40808-025-02491-z