47 citations as recorded by crossref.

1. 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
2. Global map of a comprehensive drought/flood index and analysis of controlling environmental factors J. Pang & H. Zhang 10.1007/s11069-022-05673-5
3. 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
4. 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
5. AI models uncover factors influencing scorpionism in Northern Brazil T. Moura et al. 10.1016/j.toxicon.2025.108342
6. 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
7. 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
8. 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
9. Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions J. Delaney & D. Larson 10.1111/cobi.14203
10. Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting M. Atiea et al. 10.1155/er/8022398
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. Towards a News Recommendation System to increase Reader Engagement through Newsletter Content Personalization E. Fernandes et al. 10.1016/j.procs.2024.06.165
21. Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning F. Shi et al. 10.1021/acs.iecr.2c03064
22. 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
23. Biological response of Rhopalosiphum padi and Sipha flava (Hemiptera: Aphididae) changes over generations A. Auad et al. 10.1515/flaent-2024-0088
24. 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
25. 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
26. Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. 10.5194/tc-16-4637-2022
27. 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
28. 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
29. 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
30. Research and application of XGBoost in imbalanced data P. Zhang et al. 10.1177/15501329221106935
31. 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
32. Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020 G. Li et al. 10.3390/f15020231
33. Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means A. Soubeiga et al. 10.1016/j.eswa.2024.125369
34. 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
35. Surgery duration: Optimized prediction and causality analysis O. Babayoff et al. 10.1371/journal.pone.0273831
36. Climate change enhances greening while human activities accelerate degradation in northern China's grasslands F. Cao et al. 10.1016/j.scitotenv.2025.178570
37. 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
38. Interpretable machine learning to forecast hypoxia in a lagoon D. Politikos et al. 10.1016/j.ecoinf.2021.101480
39. Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings J. Huang et al. 10.3390/buildings14092623
40. 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
41. 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
42. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models O. Babayoff et al. 10.1007/s10916-022-01902-3
43. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening Y. Yan et al. 10.1038/s41559-024-02372-1
44. Modelling of fatty acids signatures predicts macroalgal carbon in marine sediments . Erlania et al. 10.1016/j.ecolind.2024.111715
45. Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model J. Peng et al. 10.3390/rs16081361
46. 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
47. 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