{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T18:50:50Z","timestamp":1775933450676,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005645","name":"Ministry of Education and Training (MoET) in Vietnam","doi-asserted-by":"publisher","award":["B2021-MDA-13"],"award-info":[{"award-number":["B2021-MDA-13"]}],"id":[{"id":"10.13039\/501100005645","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. This research proposes and evaluates a new modeling approach based on TensorFlow deep neural networks (TFDeepNN) and geographic information systems (GIS) for forest fire danger modeling. Herein, TFDeepNN was used to create a forest fire danger model, whereas the adaptive moment estimation (ADAM) optimization algorithm was used to optimize the model, and GIS with Python programming was used to process, classify, and code the input and output. The modeling focused on the tropical forests of the Phu Yen Province (Vietnam), which incorporates 306 historical forest fire locations from 2019 to 2023 and ten forest-fire-driving factors. Random forests (RF), support vector machines (SVM), and logistic regression (LR) were used as a baseline for the model comparison. Different statistical metrics, such as F-score, accuracy, and area under the ROC curve (AUC), were employed to evaluate the models\u2019 predictive performance. According to the results, the TFDeepNN model (with F-score of 0.806, accuracy of 79.3%, and AUC of 0.873) exhibits high predictive performance and surpasses the performance of the three baseline models: RF, SVM, and LR; therefore, TFDeepNN represents a novel tool for spatially predicting forest fire danger. The forest fire danger map from this study can be helpful for policymakers and authorities in Phu Yen Province, aiding sustainable land-use planning and management.<\/jats:p>","DOI":"10.3390\/rs15143458","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:47:35Z","timestamp":1688950055000},"page":"3458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas"],"prefix":"10.3390","volume":"15","author":[{"given":"Tran Xuan","family":"Truong","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-950X","authenticated-orcid":false,"given":"Viet-Ha","family":"Nhu","sequence":"additional","affiliation":[{"name":"Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam"}]},{"given":"Doan Thi Nam","family":"Phuong","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0536-472X","authenticated-orcid":false,"given":"Le Thanh","family":"Nghi","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0836-0946","authenticated-orcid":false,"given":"Nguyen Nhu","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Cartography, Institute of Techniques for Special Engineering, Le Quy Don Technical University, Hanoi 100000, Vietnam"}]},{"given":"Pham Viet","family":"Hoa","sequence":"additional","affiliation":[{"name":"Ho Chi Minh City Institute of Resources Geography, Vietnam Academy of Science and Technology, Mac Dinh Chi 1, Ben Nghe, 1 District, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu Tien","family":"Bui","sequence":"additional","affiliation":[{"name":"GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800 B\u00f8 i Telemark, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"825190","DOI":"10.3389\/frsen.2022.825190","article-title":"Global Trends of Forest Loss Due to Fire From 2001 to 2019","volume":"3","author":"Tyukavina","year":"2022","journal-title":"Front. 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