1
|
Ma T, Wang G, Guo R, Chen L, Ma J. Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine. Journal of Environmental Management 2024; 359:120966. [PMID: 38677225 DOI: 10.1016/j.jenvman.2024.120966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/29/2024] [Accepted: 04/19/2024] [Indexed: 04/29/2024]
Abstract
Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.
Collapse
Affiliation(s)
- Tianwu Ma
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Gang Wang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China; School of Urban and Plan, Yancheng Teachers University, Yancheng, 224002, China.
| | - Rui Guo
- Administration of Zhejiang Qingliangfeng National Nature Reserve, Hangzhou, 311300, China
| | - Liang Chen
- Department of Environmental and Biological Sciences, University of Eastern Finland, Joensuu, 80101, Finland
| | - Junfei Ma
- Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China; School of Geography, Nanjing Normal University, Nanjing, 210023, China
| |
Collapse
|
2
|
Lew M, Hissong EM, Westerhoff MA, Lamps LW. Optimizing small liver biopsy specimens: a combined cytopathology and surgical pathology perspective. J Am Soc Cytopathol 2020; 9:405-421. [PMID: 32641246 DOI: 10.1016/j.jasc.2020.05.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/26/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023]
Abstract
Both fine-needle aspiration (FNA) and core needle biopsy (CNB) are widely used to obtain liver biopsy specimens, particularly from mass lesions. However, the advantages and disadvantages of FNA versus CNB in terms of appropriate use, diagnostic yield, complications, and whether or not specimens should be handled by cytopathologists, surgical pathologists, or both remain subjects of controversy. This review addresses the issues of sample adequacy, appropriate use of each technique and complications, and challenges regarding the diagnosis of both hepatic tumors and non-neoplastic liver disease.
Collapse
Affiliation(s)
- Madelyn Lew
- Department of Pathology, University of Michigan, Ann Arbor, Michigan
| | - Erika M Hissong
- Department of Pathology and Laboratory Medicine, Weill Cornell College of Medicine, New York, New York
| | | | - Laura W Lamps
- Department of Pathology, University of Michigan, Ann Arbor, Michigan.
| |
Collapse
|