1. Y. Chen, Y. Xu, and K. Zhou, "The spatial stress of urban land expansion on the water environment of the Yangtze River Delta in China," Scientific Reports, vol. 12, no. 1, p. 17011, 2022/10/11 2022, doi: 10.1038/s41598-022-21037-2. [ DOI:10.1038/s41598-022-21037-2] 2. H. Jin, S. Fang, and C. Chen, "Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series," Remote Sensing, vol. 15, no. 20, p. 4986, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/20/4986. [ DOI:10.3390/rs15204986] 3. T. Guo et al., "The Divergent Changes in Surface Water Area after the South-to-North Water Diversion Project in China," Remote Sensing, vol. 16, no. 2, p. 378, 2024. [Online]. Available: https://www.mdpi.com/2072-4292/16/2/378. [ DOI:10.3390/rs16020378] 4. W. Wang, H. Teng, L. Zhao, and L. Han, "Long-Term Changes in Water Body Area Dynamic and Driving Factors in the Middle-Lower Yangtze Plain Based on Multi-Source Remote Sensing Data," Remote Sensing, vol. 15, no. 7, p. 1816, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/7/1816. [ DOI:10.3390/rs15071816] 5. M. Saatsaz, "A historical investigation on water resources management in Iran," Environment, Development and Sustainability, vol. 22, no. 3, pp. 1749-1785, 2020/03/01 2020, doi: 10.1007/s10668-018-00307-y. [ DOI:10.1007/s10668-018-00307-y] 6. R. Noori et al., "Decline in Iran's groundwater recharge," Nature Communications, vol. 14, no. 1, p. 6674, 2023/10/21 2023, doi: 10.1038/s41467-023-42411-2. [ DOI:10.1038/s41467-023-42411-2] 7. B. Zhang, "The Climate Change, Water Crisis and Forest Ecosystem Services in Beijing, China," in Climate Change, B. Juan and K. Houshang Eds. Rijeka: IntechOpen, 2011, p. Ch. 7. 8. A. Asoka, T. Gleeson, Y. Wada, and V. Mishra, "Relative contribution of monsoon precipitation and pumping to changes in groundwater storage in India," Nature Geoscience, vol. 10, no. 2, pp. 109-117, 2017/02/01 2017, doi: 10.1038/ngeo2869. [ DOI:10.1038/ngeo2869] 9. L. Kumar and O. Mutanga, "Google Earth Engine Applications Since Inception: Usage, Trends, and Potential," Remote Sensing, vol. 10, no. 10, doi: 10.3390/rs10101509. [ DOI:10.3390/rs10101509] 10. M. Amani et al., "Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5326-5350, 2020, doi: 10.1109/JSTARS.2020.3021052. [ DOI:10.1109/JSTARS.2020.3021052] 11. H. Farhadi, A. Esmaeily, and M. Najafzadeh, "Flood monitoring by integration of Remote Sensing technique and Multi-Criteria Decision Making method," Computers & Geosciences, vol. 160, p. 105045, 2022/03/01/ 2022, doi:
https://doi.org/10.1016/j.cageo.2022.105045 [ DOI:10.1016/j.cageo.2022.105045.] 12. H. Farhadi and M. Najafzadeh, "Flood Risk Mapping by Remote Sensing Data and Random Forest Technique," Water, vol. 13, no. 21, doi: 10.3390/w13213115. [ DOI:10.3390/w13213115] 13. H. Farhadi, H. Ebadi, and A. Kiani, BADI: A NOVEL BURNED AREA DETECTION INDEX FOR SENTINEL-2 IMAGERY USING GOOGLE EARTH ENGINE PLATFORM. 2023, pp. 179-186. [ DOI:10.5194/isprs-annals-X-4-W1-2022-179-2023] 14. H. Farhadi, M. Mokhtarzade, H. Ebadi, and B. A. Beirami, "Rapid and automatic burned area detection using sentinel-2 time-series images in google earth engine cloud platform: a case study over the Andika and Behbahan Regions, Iran," (in eng), Environ Monit Assess, vol. 194, no. 5, p. 369, Apr 16 2022, doi: 10.1007/s10661-022-10045-4. [ DOI:10.1007/s10661-022-10045-4] 15. O. D. Mofokeng, S. A. Adelabu, and C. M. Jackson, "An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques," Fire, vol. 7, no. 2, doi: 10.3390/fire7020061. [ DOI:10.3390/fire7020061] 16. R. Farzanmanesh, K. Khoshelham, L. Volkova, S. Thomas, J. Ravelonjatovo, and C. J. Weston, "Temporal Analysis of Mangrove Forest Extent in Restoration Initiatives: A Remote Sensing Approach Using Sentinel-2 Imagery," Forests, vol. 15, no. 3, doi: 10.3390/f15030399. [ DOI:10.3390/f15030399] 17. D. Huang et al., "Mapping Paddy Rice in Rice-Wetland Coexistence Zone by Integrating Sentinel-1 and Sentinel-2 Data," Agriculture, vol. 14, no. 3, doi: 10.3390/agriculture14030345. [ DOI:10.3390/agriculture14030345] 18. H. Farhadi, H. Ebadi, and A. Kiani, "F2BFE: development of feature-based building footprint extraction by remote sensing data and GEE," International Journal of Remote Sensing, vol. 44, no. 19, pp. 5845-5875, 2023/10/02 2023, doi: 10.1080/01431161.2023.2255351. [ DOI:10.1080/01431161.2023.2255351] 19. J. Liang, Y. Xie, Z. Sha, and A. Zhou, "Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)," Computers, Environment and Urban Systems, vol. 84, p. 101542, 2020/11/01/ 2020, doi:
https://doi.org/10.1016/j.compenvurbsys.2020.101542 [ DOI:10.1016/j.compenvurbsys.2020.101542.] 20. H. Farhadi, T. Managhebi, and H. Ebadi, "Buildings extraction in urban areas based on the radar and optical time series data using Google Earth Engine," (in en), Scientific- Research Quarterly of Geographical Data (SEPEHR), vol. 30, no. 120, pp. 43-63, 2022, doi: 10.22131/sepehr.2022.251053. 21. H. Farhadi, H. Ebadi, A. Kiani, and A. Asgary, "A novel flood/water extraction index (FWEI) for identifying water and flooded areas using sentinel-2 visible and near-infrared spectral bands," Stochastic Environmental Research and Risk Assessment, vol. 38, no. 5, pp. 1873-1895, 2024/05/01 2024, doi: 10.1007/s00477-024-02660-z. [ DOI:10.1007/s00477-024-02660-z] 22. Y. Pang et al., "Remote Sensing Extraction of Lakes on the Tibetan Plateau Based on the Google Earth Engine and Deep Learning," Remote Sensing, vol. 16, no. 3, doi: 10.3390/rs16030583. [ DOI:10.3390/rs16030583] 23. J. Zhou, L. Ke, X. Ding, R. Wang, and F. Zeng, "Monitoring Spatial-Temporal Variations in River Width in the Aral Sea Basin with Sentinel-2 Imagery," Remote Sensing, vol. 16, no. 5, doi: 10.3390/rs16050822. [ DOI:10.3390/rs16050822] 24. J. Li, B. Peng, Y. Wei, and H. Ye, "Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2," PLOS ONE, vol. 16, p. e0253209, 06/18 2021, doi: 10.1371/journal.pone.0253209. [ DOI:10.1371/journal.pone.0253209] 25. C. Xie, X. Huang, W. Zeng, and X. Fang, "A novel water index for urban high-resolution eight-band WorldView-2 imagery," International Journal of Digital Earth, vol. 9, no. 10, pp. 925-941, 2016/10/02 2016, doi: 10.1080/17538947.2016.1170215. [ DOI:10.1080/17538947.2016.1170215] 26. J. Li et al., "Satellite Detection of Surface Water Extent: A Review of Methodology," Water, vol. 14, no. 7, doi: 10.3390/w14071148. [ DOI:10.3390/w14071148] 27. X. Wang et al., "A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery," International Journal of Applied Earth Observation and Geoinformation, vol. 68, pp. 73-91, 2018/06/01/ 2018, doi:
https://doi.org/10.1016/j.jag.2018.01.018 [ DOI:10.1016/j.jag.2018.01.018.] 28. H. Liu, H. Hu, X. Liu, H. Jiang, W. Liu, and X. Yin, "A Comparison of Different Water Indices and Band Downscaling Methods for Water Bodies Mapping from Sentinel-2 Imagery at 10-M Resolution," Water, vol. 14, no. 17, doi: 10.3390/w14172696. [ DOI:10.3390/w14172696] 29. H. Tang, S. Lu, M. H. Ali Baig, M. Li, C. Fang, and Y. Wang, "Large-Scale Surface Water Mapping Based on Landsat and Sentinel-1 Images," Water, vol. 14, no. 9, doi: 10.3390/w14091454. [ DOI:10.3390/w14091454] 30. L. Wentao, Y. Qiuze, and Y. Wenxian, "Water extraction in SAR images using GLCM and Support Vector Machine," in IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, 24-28 Oct. 2010 2010, pp. 740-743, doi: 10.1109/ICOSP.2010.5655766. [ DOI:10.1109/ICOSP.2010.5655766] 31. J. Guo, X. Wang, B. Liu, K. Liu, Y. Zhang, and C. Wang, "Remote-Sensing Extraction of Small Water Bodies on the Loess Plateau," Water, vol. 15, no. 5, p. 866, 2023. [Online]. Available: https://www.mdpi.com/2073-4441/15/5/866. [ DOI:10.3390/w15050866] 32. S. K. McFeeters, "The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features," International Journal of Remote Sensing, vol. 17, no. 7, pp. 1425-1432, 1996/05/01 1996, doi: 10.1080/01431169608948714. [ DOI:10.1080/01431169608948714] 33. H. Xu, "Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery," International Journal of Remote Sensing, vol. 27, no. 14, pp. 3025-3033, 2006/07/20 2006, doi: 10.1080/01431160600589179. [ DOI:10.1080/01431160600589179] 34. C. A. O. Rong-Long, C. J. Li, L. Y. Liu, J. H. Wang, and G. J. Yan, "Extracting Miyun Reservoir's Water Area and Monitoring Its Change Based on a Revised Normalized Different Water Index," Science of Surveying and Mapping, vol. 59, 2008. 35. Y. Pei, "A Study on Information Extraction of Water System in Semi-arid Regions with the Enhanced Water Index(EWI) and GIS Based Noise Remove Techniques," Remote Sensing Information, 2007. 36. A. Fisher, N. Flood, and T. Danaher, "Comparing Landsat water index methods for automated water classification in eastern Australia," Remote Sensing of Environment, vol. 175, pp. 167-182, 2016/03/15/ 2016, doi:
https://doi.org/10.1016/j.rse.2015.12.055 [ DOI:10.1016/j.rse.2015.12.055.] 37. L. Shen and C. Li, "Water body extraction from Landsat ETM+ imagery using adaboost algorithm," in 2010 18th International Conference on Geoinformatics, 18-20 June 2010 2010, pp. 1-4, doi: 10.1109/GEOINFORMATICS.2010.5567762. [ DOI:10.1109/GEOINFORMATICS.2010.5567762] 38. J. Chen, T. Kang, S. Yang, J. Bu, K. Cao, and Y. Gao, "Open-Surface Water Bodies Dynamics Analysis in the Tarim River Basin (North-Western China), Based on Google Earth Engine Cloud Platform," Water, vol. 12, no. 10, p. 2822, 2020. [Online]. Available: https://www.mdpi.com/2073-4441/12/10/2822. [ DOI:10.3390/w12102822] 39. X. Yang, Y. Li, Y. Wei, Z. Chen, and P. Xie, "Water Body Extraction from Sentinel-3 Image with Multiscale Spatiotemporal Super-Resolution Mapping," Water, vol. 12, no. 9, doi: 10.3390/w12092605. [ DOI:10.3390/w12092605] 40. P. Rao, W. Jiang, Y. Hou, Z. Chen, and K. Jia, "Dynamic Change Analysis of Surface Water in the Yangtze River Basin Based on MODIS Products," Remote Sensing, vol. 10, no. 7, doi: 10.3390/rs10071025. [ DOI:10.3390/rs10071025] 41. J. Chen et al., "The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis," Remote Sensing, vol. 16, no. 11, doi: 10.3390/rs16111984. [ DOI:10.3390/rs16111984] 42. N. Huda, T. Terao, A. Nonomura, and Y. Suenaga, "Time-Series Remote Sensing Study to Detect Surface Water Seasonality and Local Water Management at Upper Reaches of Southwestern Bengal Delta from 1972 to 2020," Sustainability, vol. 13, no. 17, doi: 10.3390/su13179798. [ DOI:10.3390/su13179798] 43. L. Liuzzo, V. Puleo, S. Nizza, and G. Freni, "Parameterization of a Bayesian Normalized Difference Water Index for Surface Water Detection," Geosciences, vol. 10, no. 7, doi: 10.3390/geosciences10070260. [ DOI:10.3390/geosciences10070260] 44. S. L. K. Reddy, C. V. Rao, P. R. Kumar, R. V. G. Anjaneyulu, and B. G. Krishna, "A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY," Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., vol. XLII-5, pp. 323-328, 2018, doi: 10.5194/isprs-archives-XLII-5-323-2018. [ DOI:10.5194/isprs-archives-XLII-5-323-2018] 45. S. Liu, Y. Wu, G. Zhang, N. Lin, and Z. Liu, "Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province," Remote Sensing, vol. 15, no. 6, doi: 10.3390/rs15061678. [ DOI:10.3390/rs15061678] 46. M. Ghaheri, M. H. Baghal-Vayjooee, and J. Naziri, "Lake Urmia, Iran: A summary review," International Journal of Salt Lake Research, vol. 8, no. 1, pp. 19-22, 1999/03/01 1999, doi: 10.1007/BF02442134. [ DOI:10.1007/BF02442134] 47. E. Ministry of, "Guide Lines and Criteria for Classification and Coding Basin and Study Areas in Iran," 310, 2012. 48. S. Sima, A. Ahmadalipour, and M. Tajrishy, "Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation," Remote Sensing of Environment, vol. 136, pp. 374-385, 2013/09/01/ 2013, doi:
https://doi.org/10.1016/j.rse.2013.05.014 [ DOI:10.1016/j.rse.2013.05.014.] 49. H. Emami and A. Zarei, "Modelling lake water's surface changes using environmental and remote sensing data: A case study of lake urmia," Remote Sensing Applications: Society and Environment, vol. 23, p. 100594, 2021/08/01/ 2021, doi:
https://doi.org/10.1016/j.rsase.2021.100594 [ DOI:10.1016/j.rsase.2021.100594.] 50. M. A. Wulder et al., "Current status of Landsat program, science, and applications," Remote Sensing of Environment, vol. 225, pp. 127-147, 2019/05/01/ 2019, doi:
https://doi.org/10.1016/j.rse.2019.02.015 [ DOI:10.1016/j.rse.2019.02.015.] 51. L. Yang, J. Driscol, S. Sarigai, Q. Wu, H. Chen, and C. D. Lippitt, "Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review," Remote Sensing, vol. 14, no. 14, doi: 10.3390/rs14143253. [ DOI:10.3390/rs14143253] 52. J. Wang, L. Wang, M. Li, L. Zhu, and X. Li, "Lake volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019," Scientific Data, vol. 9, no. 1, p. 611, 2022/10/08 2022, doi: 10.1038/s41597-022-01711-w. [ DOI:10.1038/s41597-022-01711-w] 53. A. Dervisoglu, "Investigation of Long and Short-Term Water Surface Area Changes in Coastal Ramsar Sites in Turkey with Google Earth Engine," ISPRS International Journal of Geo-Information, vol. 11, no. 1, doi: 10.3390/ijgi11010046. [ DOI:10.3390/ijgi11010046] 54. J. W. Rouse, Jr., R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring Vegetation Systems in the Great Plains with Erts," in NASA Special Publication, vol. 351, 1974, p. 309. 55. G. L. Feyisa, H. Meilby, R. Fensholt, and S. Proud, "Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery," Remote Sensing of Environment, vol. 140, pp. 23-35, 01/31 2014, doi: 10.1016/j.rse.2013.08.029. [ DOI:10.1016/j.rse.2013.08.029] 56. S. Huang, L. Tang, J. P. Hupy, Y. Wang, and G. Shao, "A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing," Journal of Forestry Research, vol. 32, no. 1, pp. 1-6, 2021/02/01 2021, doi: 10.1007/s11676-020-01155-1. [ DOI:10.1007/s11676-020-01155-1] 57. M. Schnur, H. Xie, and X. Wang, "Estimating root zone soil moisture at distant sites using MODIS NDVI and EVI in a semi-arid region of southwestern USA," Ecological Informatics, vol. 5, pp. 400-409, 09/01 2010, doi: 10.1016/j.ecoinf.2010.05.001. [ DOI:10.1016/j.ecoinf.2010.05.001] 58. T. D. Acharya, A. Subedi, and D. H. Lee, "Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal," (in eng), Sensors (Basel), vol. 18, no. 8, Aug 7 2018, doi: 10.3390/s18082580. [ DOI:10.3390/s18082580] 59. J. Li et al., "Accurate water extraction using remote sensing imagery based on normalized difference water index and unsupervised deep learning," Journal of Hydrology, vol. 612, p. 128202, 2022/09/01/ 2022, doi:
https://doi.org/10.1016/j.jhydrol.2022.128202 [ DOI:10.1016/j.jhydrol.2022.128202.] 60. R. P. Ji, W. Y. Yu, R. Feng, J. W. Wu, and Y. S. Zhang, "The threshold determination methods of water body information extraction using GF-1 satellite image," IOP Conference Series: Materials Science and Engineering, vol. 592, no. 1, p. 012088, 2019/08/01 2019, doi: 10.1088/1757-899X/592/1/012088. [ DOI:10.1088/1757-899X/592/1/012088] 61. X. Dong, C. Hu, and Y. Zhao, "Novel Threshold Self-Regulating Water Extraction Method," Journal of Hydrologic Engineering, vol. 28, no. 8, p. 04023020, 2023/08/01 2023, doi: 10.1061/JHYEFF.HEENG-5891. [ DOI:10.1061/JHYEFF.HEENG-5891] 62. M. K. Kolli, C. Opp, D. Karthe, and B. Pradhan, "Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine - the case study of Kolleru Lake, South India," Geocarto International, vol. 37, no. 26, pp. 11173-11189, 2022/12/13 2022, doi: 10.1080/10106049.2022.2046872. [ DOI:10.1080/10106049.2022.2046872] 63. N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979, doi: 10.1109/TSMC.1979.4310076. [ DOI:10.1109/TSMC.1979.4310076] 64. J. Tan et al., "A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block," Remote Sensing, vol. 15, no. 10, doi: 10.3390/rs15102690. [ DOI:10.3390/rs15102690] 65. H. Cao, H. Zhang, C. Wang, and B. Zhang, "Operational flood detection using Sentinel-1 SAR data over large areas," Water, vol. 11, no. 4, p. 786, 2019. [ DOI:10.3390/w11040786] 66. K. N. Markert et al., "Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine," Remote Sensing, vol. 12, no. 15, doi: 10.3390/rs12152469. [ DOI:10.3390/rs12152469] 67. P. Rambabu and C. Nagaraju, "The optimal thresholding technique for image segmentation using fuzzy ostu method," International Journal of Applied Engineering Research, vol. 10, no. 13, pp. 33842-33846, 2015. 68. W. Li, D. Li, and Z. N. Fang, "Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood," Hydrology, vol. 10, no. 1, doi: 10.3390/hydrology10010017. [ DOI:10.3390/hydrology10010017] 69. G. Donchyts, J. Schellekens, H. Winsemius, E. Eisemann, and N. Van de Giesen, "A 30 m Resolution Surface Water Mask Including Estimation of Positional and Thematic Differences Using Landsat 8, SRTM and OpenStreetMap: A Case Study in the Murray-Darling Basin, Australia," Remote Sensing, vol. 8, no. 5, doi: 10.3390/rs8050386. [ DOI:10.3390/rs8050386] 70. T. Gargula, "Adjustment of an Integrated Geodetic Network Composed of GNSS Vectors and Classical Terrestrial Linear Pseudo-Observations," Applied Sciences, vol. 11, no. 10, doi: 10.3390/app11104352. [ DOI:10.3390/app11104352] 71. J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986, doi: 10.1109/TPAMI.1986.4767851. [ DOI:10.1109/TPAMI.1986.4767851] 72. G. H. Rosenfield and K. Fitzpatrick-Lins, "A coefficient of agreement as a measure of thematic classification accuracy," Photogrammetric Engineering and Remote Sensing, vol. 52, no. 2, pp. 223-227, 1986. [Online]. Available: https://pubs.usgs.gov/publication/70014667. 73. R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sensing of Environment, vol. 37, no. 1, pp. 35-46, 1991/07/01/ 1991, doi:
https://doi.org/10.1016/0034-4257(91)90048-B [ DOI:10.1016/0034-4257(91)90048-B.] 74. M. Story and R. G. Congalton, "Accuracy assessment: a user's perspective," Photogrammetric Engineering and Remote Sensing, vol. 52, pp. 397-399, 1986. 75. A. Barsi, Z. Kugler, I. László, G. Szabó, and H. Abdulmuttalib, "ACCURACY DIMENSIONS IN REMOTE SENSING," ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-3, pp. 61-67, 04/30 2018, doi: 10.5194/isprs-archives-XLII-3-61-2018. [ DOI:10.5194/isprs-archives-XLII-3-61-2018] 76. C. Goutte and E. Gaussier, "A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation," in Advances in Information Retrieval, Berlin, Heidelberg, D. E. Losada and J. M. Fernández-Luna, Eds., 2005// 2005: Springer Berlin Heidelberg, pp. 345-359. [ DOI:10.1007/978-3-540-31865-1_25] 77. C. J. Van Rijsbergen, Information Retrieval. Butterworths, 1979. 78. M. Heydarian, T. Doyle, and R. Samavi, "MLCM: Multi-Label Confusion Matrix," IEEE Access, vol. 10, 02/01 2022, doi: 10.1109/ACCESS.2022.3151048. [ DOI:10.1109/ACCESS.2022.3151048]
|