Artificial intelligence and machine learning applications in forest management and biodiversity conservation

Asif Raihan

Article ID: 3825
Vol 6, Issue 2, 2023

VIEWS - 3927 (Abstract)

Abstract


The recent progress in data science, along with the transformation in digital and satellite technology, has enhanced the capacity for artificial intelligence (AI) applications in the forestry and wildlife domains. Nevertheless, the swift proliferation of developmental projects, agricultural, and urban areas pose a significant threat to biodiversity on a global scale. Hence, the integration of emerging technologies such as AI in the fields of forests and biodiversity might facilitate the efficient surveillance, administration, and preservation of biodiversity and forest resources. The objective of this paper is to present a comprehensive review of how AI and machine learning (ML) algorithms are utilized in the forestry sector and biodiversity conservation worldwide. Furthermore, this research examines the difficulties encountered while implementing AI technology in the fields of forestry and biodiversity. Enhancing the availability of extensive data pertaining to forests and biodiversity, along with the utilization of cloud computing and digital and satellite technology, can facilitate the wider acceptance and implementation of AI technology. The findings of this study would inspire forest officials, scientists, researchers, and conservationists to investigate the potential of AI technology for the purposes of forest management and biodiversity conservation.


Keywords


natural resources; forest; biodiversity conservation; artificial intelligence; machine learning

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References


Raihan A. A comprehensive review of artificial intelligence and machine learning applications in energy sector. Journal of Technology Innovations and Energy 2023; 2(4): 1–26. doi: 10.56556/jtie.v2i4.608 Raihan A. A comprehensive review of the recent advancement in integrating deep learning with geographic information systems. Research Briefs on Information & Communication Technology Evolution 2023; 9(6): 98–115. doi: 10.56801/rebicte.v9i.160 Raihan A. An overview of the implications of artificial intelligence (AI) in Sixth Generation (6G) communication network. Research Briefs on Information & Communication Technology Evolution 2023; 9(8): 120–146. doi: 10.56801/rebicte.v9i.164 Gomes C, Dietterich T, Barrett C, et al. Computational sustainability: Computing for a better world and a sustainable future. Communications of the ACM 2019; 62(9): 56–65. doi: 10.1145/3339399 Christin S, Hervet É, Lecomte N. Applications for deep learning in ecology. Methods in Ecology and Evolution 2019; 10(10): 1632–1644. doi: 10.1111/2041-210X.13256 Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 2019; 2: 1–12. doi: 10.1016/j.aiia.2019.05.004 Lamba A, Cassey P, Segaran RR, Koh LP. Deep learning for environmental conservation. Current Biology 2019; 29(19): R977–R982. doi: 10.1016/j.cub.2019.08.016 Raihan A. Economy-energy-environment nexus: The role of information and communication technology towards green development in Malaysia. Innovation and Green Development 2023; 2(4): 100085. doi: 10.1016/j.igd.2023.100085 Raihan A. Toward sustainable and green development in Chile: Dynamic influences of carbon emission reduction variables. Innovation and Green Development 2023; 2(2): 100038. doi: 10.1016/j.igd.2023.100038 Raihan A. A review of the global climate change impacts, adaptation strategies, and mitigation options in the socio-economic and environmental sectors. Journal of Environmental Science and Economics 2023; 2(3): 36–58. doi: 10.56556/jescae.v2i3.587 Khan S, Gupta PK. Comparitive study of tree counting algorithms in dense and sparse vegetative regions. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018; 42(5): 801–808. doi: 10.5194/isprs-archives-XLII-5-801-2018 Fromm M, Schubert M, Castilla G, et al. Automated detection of conifer seedlings in drone imagery using convolutional neural networks. Remote Sensing 2019; 11(21): 2585. doi: 10.3390/rs11212585 Wood CM, Gutiérrez RJ, Zachariah Peery M. Acoustic monitoring reveals a diverse forest owl community, illustrating its potential for basic and applied ecology. Ecology 2019; 100(9): e02764. doi: 10.1002/ecy.2764 Nay J, Burchfield E, Gilligan J. A machine-learning approach to forecasting remotely sensed vegetation health. International Journal of Remote Sensing 2018; 39(6): 1800–1816. doi: 10.1080/01431161.2017.1410296 Burivalova Z, Game ET, Butler RA. The sound of a tropical forest. Science 2019; 363(6422): 28–29. doi: 10.1126/science.aav1902 Metcalf OC, Ewen JG, McCready M, et al. A novel method for using ecoacoustics to monitor post‐translocation behaviour in an endangered passerine. Methods in Ecology and Evolution 2019; 10(5): 626–636. doi: 10.1111/2041-210X.13147 Raihan A. The dynamic nexus between economic growth, renewable energy use, urbanization, industrialization, tourism, agricultural productivity, forest area, and carbon dioxide emissions in the Philippines. Energy Nexus 2023; 9: 100180. doi: 10.1016/j.nexus.2023.100180 Raihan A. The contribution of economic development, renewable energy, technical advancements, and forestry to Uruguay’s objective of becoming carbon neutral by 2030. Carbon Research 2023; 2: 20. doi: 10.1007/s44246-023-00052-6 Raihan A. A review on the integrative approach for economic valuation of forest ecosystem services. Journal of Environmental Science and Economics 2023; 2(3): 1–18. doi: 10.56556/jescae.v2i3.554 Raihan A. Sustainable development in Europe: A review of the forestry sector’s social, environmental, and economic dynamics. Global Sustainability Research 2023; 2(3): 72–92. doi: 10.56556/gssr.v2i3.585 Raihan A. The potential of agroforestry in South Asian countries towards achieving the climate goals. Asian Journal of Forestry 2024; 8(1): 1–17. doi: 10.13057/asianjfor/r080101 Raihan A, Bijoy TR. A review of the industrial use and global sustainability of Cannabis sativa. Global Sustainability Research 2023; 2(4): 1–29. doi: 10.56556/gssr.v2i4.597 Raihan A. The influences of renewable energy, globalization, technological innovations, and forests on emission reduction in Colombia. Innovation and Green Development 2023; 2(4): 100071. doi: 10.1016/j.igd.2023.100071 Raihan A. A concise review of technologies for converting forest biomass to bioenergy. Journal of Technology Innovations and Energy 2023; 2(3): 10–36. doi: 10.56556/jtie.v2i3.592 Raihan A. A review of tropical blue carbon ecosystems for climate change mitigation. Journal of Environmental Science and Economics 2023; 2(4): 14–36. doi: 10.56556/jescae.v2i4.602 Curtis PG, Slay CM, Harris NL, et al. Classifying drivers of global forest loss. Science 2018; 361(6407): 1108–1111. doi: 10.1126/science.aau3445 Vir Sharma J. Forestry sector in India is net source of green house gases (GHGS). Journal of Environmental Science and Engineering Technology 2017; 5: 2–7. Garske B, Bau A, Ekardt F. Digitalization and AI in European agriculture: A strategy for achieving climate and biodiversity targets? Sustainability 2021; 13(9): 4652. doi: 10.3390/su13094652 Rana P, Miller DC. Machine learning to analyze the social-ecological impacts of natural resource policy: insights from community forest management in the Indian Himalaya. Environmental Research Letters 2019; 14(2): 024008. doi: 10.1088/1748-9326/aafa8f Liu Z, Peng C, Work T, et al. Application of machine-learning methods in forest ecology: recent progress and future challenges. Environmental Reviews 2018; 26(4): 339–350. doi: 10.1139/er-2018-0034 Dou X, Yang Y, Luo J. Estimating forest carbon fluxes using machine learning techniques based on eddy covariance measurements. Sustainability 2018; 10(1): 203. doi: 10.3390/su10010203 Mou C, Liang A, Hu C, et al. Monitoring endangered and rare wildlife in the field: A foundation deep learning model integrating human knowledge for incremental recognition with few data and low cost. Animals 2023; 13(20): 3168. doi: 10.3390/ani13203168 Padovese BT, Padovese LR. Machine learning for identifying an endangered Brazilian Psittacidae species. Journal of Environmental Informatics Letters 2019; 2(1): 19–27. doi: 10.3808/jeil.201900013 Lavorgna A, Middleton SE, Pickering B, et al. FloraGuard: Tackling the online illegal trade in endangered plants through a cross-disciplinary ICT-enabled methodology. Journal of Contemporary Criminal Justice 2020; 36(3): 428–450. doi: 10.1177/1043986220910297 Di Minin E, Fink C, Hiippala T, Tenkanen H. A framework for investigating illegal wildlife trade on social media with machine learning. Conservation Biology 2019; 33(1): 210–213. doi: 10.1111/cobi.13104 Di Minin E, Fink C, Tenkanen H, Hiippala T. Machine learning for tracking illegal wildlife trade on social media. Nature Ecology & Evolution 2018; 2: 406–407. doi: 10.1038/s41559-018-0466-x Borowicz A, Le H, Humphries G, et al. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS One 2019; 14(10): e0212532. doi: 10.1371/journal.pone.0212532 Wäldchen J, Rzanny M, Seeland M, Mäder P. Automated plant species identification—Trends and future directions. PLoS Computational Biology 2018; 14(4): e1005993. doi: 10.1371/journal.pcbi.1005993 Willi M, Pitman RT, Cardoso AW, et al. Identifying animal species in camera trap images using deep learning and citizen science. Methods in Ecology and Evolution 2019; 10(1): 80–91. doi: 10.1111/2041-210X.13099 Botto Nuñez G, Lemus G, Muñoz Wolf M, et al. The first artificial intelligence algorithm for identification of bat species in Uruguay. Ecological Informatics 2018; 46: 97–102. doi: 10.1016/j.ecoinf.2018.05.005 Azlah MAF, Chua LS, Rahmad FR, et al. Review on techniques for plant leaf classification and recognition. Computers 2019; 8(4): 77. doi: 10.3390/computers8040077 Larrea‐Gallegos G, Vázquez‐Rowe I. Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects. Journal of Industrial Ecology 2022; 26(1): 225–239. doi: 10.1111/jiec.13185 Mayfield HJ, Smith C, Gallagher M, Hockings M. Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling & Software 2020; 131: 104741. doi: 10.1016/j.envsoft.2020.104741 Dominguez D, del Villar LDJ, Pantoja O, González-Rodríguez M. Forecasting Amazon rain-forest deforestation using a hybrid machine learning model. Sustainability 2022; 14(2): 691. doi: 10.3390/su14020691 Giannetti F, Barbati A, Mancini LD, et al. European forest types: Toward an automated classification. Annals of Forest Science 2018; 75(1): 6. doi: 10.1007/s13595-017-0674-6 Lin P, Lu Q, Li D, et al. Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network. Natural Resource Modeling 2020; 33(1): e12248. doi: 10.1111/nrm.12248 Tian L, Wu X, Tao Y, et al. Review of remote sensing-based methods for forest aboveground biomass estimation: Progress, challenges, and prospects. Forests 2023; 14(6):1086. doi: 10.3390/f14061086 Bastin JF, Finegold Y, Garcia C, et al. The global tree restoration potential. Science 2019; 365(6448): 76–79. doi: 10.1126/science.aax0848 Adikari KE, Shrestha S, Ratnayake DT, et al. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environmental Modelling & Software 2021; 144: 105136. doi: 10.1016/j.envsoft.2021.105136 Jaafari A, Zenner EK, Panahi M, Shahabi H. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and forest meteorology 2019; 266–267: 198–207. doi: 10.1016/j.agrformet.2018.12.015 Raihan A. An econometric evaluation of the effects of economic growth, energy use, and agricultural value added on carbon dioxide emissions in Vietnam. Asia-Pacific Journal of Regional Science 2023; 7: 665–696. doi: 10.1007/s41685-023-00278-7 Raihan A. An econometric assessment of the relationship between meat consumption and greenhouse gas emissions in the United States. Environmental Processes 2023; 10(2): 32. doi: 10.1007/s40710-023-00650-x Zhang G, Wang M, Liu K. Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China. International Journal of Disaster Risk Science 2019; 10: 386–403. doi: 10.1007/s13753-019-00233-1 Golhani K, Balasundram SK, Vadamalai G, Pradhan B. A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture 2018; 5(3): 354–371. doi: 10.1016/j.inpa.2018.05.002 Rammer W, Seidl R. Harnessing deep learning in ecology: An example predicting bark beetle outbreaks. Frontiers in Plant Science 2019; 10: 1327. doi: 10.3389/fpls.2019.01327 Wiesner-Hanks T, Wu H, Stewart E, et al. Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Frontiers in Plant Science 2019; 10: 1550. doi: 10.3389/fpls.2019.01550 Raihan A. The influence of tourism on the road to achieving carbon neutrality and environmental sustainability in Malaysia: The role of renewable energy. Sustainability Analytics and Modeling 2024; 4: 100028. doi: 10.1016/j.samod.2023.100028 Raihan A. The influence of meat consumption on greenhouse gas emissions in Argentina. Resources, Conservation & Recycling Advances 2023; 19: 200183. doi: 10.1016/j.rcradv.2023.200183 Backs JAJ, Nychka JA, St. Clair CC. Warning systems triggered by trains increase flight-initiation times of wildlife. Transportation Research Part D: Transport and Environment 2020; 87: 102502. doi: 10.1016/j.trd.2020.102502 Shi C, Liu D, Cui Y, et al. Amur tiger stripes: Individual identification based on deep convolutional neural network. Integrative Zoology 2020; 15(6): 461–470. doi: 10.1111/1749-4877.12453 Milovanović MB, Antić DS, Rajić MN, et al. Wood resource management using an endocrine NARX neural network. European Journal of Wood and Wood Products 2018; 76: 687–697. doi: 10.1007/s00107-017-1223-6 Guswa AJ, Tetzlaff D, Selker JS, et al. Advancing ecohydrology in the 21st century: A convergence of opportunities. Ecohydrology 2020; 13(4): e2208. doi: 10.1002/eco.2208 Zhang F, Wu S, Liu J, et al. Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning. Soil Science Society of America Journal 2021; 85(4): 989–1001. doi: 10.1002/saj2.20193 Lee CS, Sohn E, Park JD, Jang J-D. Estimation of soil moisture using deep learning based on satellite data: A case study of South Korea. GIScience & Remote Sensing 2019; 56(1): 43–67. doi: 10.1080/15481603.2018.1489943 de Oliveira VA, Rodrigues AF, Morais MAV, et al. Spatiotemporal modelling of soil moisture in an A tlantic forest through machine learning algorithms. European Journal of Soil Science 2021; 72(5): 1969–1987. doi: 10.1111/ejss.13123 Pan S, Pan N, Tian H, et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrology and Earth System Sciences 2020; 24(3): 1485–1509. doi: 10.5194/hess-24-1485-2020 Panda S, Amatya DM, Jackson R, et al. Automated geospatial models of varying complexities for pine forest evapotranspiration estimation with advanced data mining. Water 2018; 10(11): 1687. doi: 10.3390/w10111687 Luo XR, Li SD, Liu L, et al. Quantifying aboveground vegetation water storage combining Landsat 8 OLI and Sentinel-1 imageries. Geocarto International 2022; 37(9): 2717–2734. doi: 10.1080/10106049.2020.1861662 Irrgang C, Saynisch‐Wagner J, Dill R, et al. Self‐validating deep learning for recovering terrestrial water storage from gravity and altimetry measurements. Geophysical Research Letters 2020; 47(17): e2020GL089258. doi: 10.1029/2020GL089258 Bhanja SN, Malakar P, Mukherjee A, et al. Using satellite‐based vegetation cover as indicator of groundwater storage in natural vegetation areas. Geophysical Research Letters 2019; 46(14): 8082–8092. doi: 10.1029/2019GL083015 Kamarudin MH, Ismail ZH, Saidi NB. Deep learning sensor fusion in plant water stress assessment: A comprehensive review. Applied Sciences 2021; 11(4): 1403. doi: 10.3390/app11041403 Pal S, Sharma P. A review of machine learning applications in land surface modeling. Earth 2021; 2(1): 174–190. doi: 10.3390/earth2010011 Dikshit A, Pradhan B, Alamri AM. Pathways and challenges of the application of artificial intelligence to geohazards modelling. Gondwana Research 2021; 100: 290–301. doi: 10.1016/j.gr.2020.08.007 Gonzales-Inca C, Calle M, Croghan D, et al. Geospatial artificial intelligence (GeoAI) in the integrated hydrological and fluvial systems modeling: Review of current applications and trends. Water 2022; 14(14): 2211. doi: 10.3390/w14142211 Fathian F, Mehdizadeh S, Sales AK, Safari MJS. Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models. Journal of Hydrology 2019; 575: 1200–1213. doi: 10.1016/j.jhydrol.2019.06.025 Ceccaroni L, Velickovski F, Blaas M, et al. Artificial intelligence and earth observation to explore water quality in the Wadden Sea. In: Mathieu PP, Aubrecht C (editors). Earth Observation Open Science and Innovation. Springer, Cham; 2018. pp. 311–320. doi: 10.1007/978-3-319-65633-5_18 Elkiran G, Nourani V, Abba SI. Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology 2019; 577: 123962. doi: 10.1016/j.jhydrol.2019.123962 Fijani E, Barzegar R, Deo R, et al. Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Science of The Total Environment 2019; 648: 839–853. doi: 10.1016/j.scitotenv.2018.08.221 Gunda NSK, Gautam SH, Mitra SK. Artificial intelligence based mobile application for water quality monitoring. Journal of The Electrochemical Society 2019; 166(9): B3031. doi: 10.1149/2.0081909jes Rajaee T, Khani S, Ravansalar M. Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems 2020; 200: 103978. doi: 10.1016/j.chemolab.2020.103978 Tiyasha, Tung TM, Yaseen ZM. A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology 2020; 585: 124670. doi: 10.1016/j.jhydrol.2020.124670 Franceschini S, Mattei F, D’Andrea L, et al. Rummaging through the bin: Modelling marine litter distribution using artificial neural networks. Marine Pollution Bulletin 2019; 149: 110580. doi: 10.1016/j.marpolbul.2019.110580 Wang P, Yao J, Wang G, et al. Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Science of the Total Environment 2019; 693: 133440. doi: 10.1016/j.scitotenv.2019.07.246 Kroodsma DA, Mayorga J, Hochberg T, et al. Tracking the global footprint of fisheries. Science 2018; 359(6378): 904–908. doi: 10.1126/science.aao564 Hu J-H, Tsai W-P, Cheng S-T, Chang F-J. Explore the relationship between fish community and environmental factors by machine learning techniques. Environmental Research 2020; 184: 109262. doi: 10.1016/j.envres.2020.109262 Russo T, Franceschini S, D’Andrea L, et al. Predicting fishing footprint of trawlers from environmental and fleet data: an application of artificial neural networks. Frontiers in Marine Science 2019; 6: 670. doi: 10.3389/fmars.2019.00670 Guénard G, Morin J, Matte P, et al. Deep learning habitat modeling for moving organisms in rapidly changing estuarine environments: A case of two fishes. Estuarine, Coastal and Shelf Science 2020; 238: 106713. doi: 10.1016/j.ecss.2020.106713 Nunes JACC, Cruz ICS, Nunes A, Pinheiro HT. Speeding up coral reef conservation with AI-aided automated image analysis. Nature Machine Intelligence 2020; 2: 292. doi: 10.1038/s42256-020-0192-3 Allken V, Handegard NO, Rosen S, et al. Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science 2019; 76(1): 342–349. doi: 10.1093/icesjms/fsy147 Álvarez-Ellacuría A, Palmer M, Catalán IA, Lisani JL. Image-based, unsupervised estimation of fish size from commercial landings using deep learning. ICES Journal of Marine Science 2020; 77(4): 1330–1339. doi: 10.1093/icesjms/fsz216 Gray PC, Fleishman AB, Klein DJ, et al. A convolutional neural network for detecting sea turtles in drone imagery. Methods in Ecology and Evolution 2019; 10(3): 345–355. doi: 10.1111/2041-210X.13132 Labao AB, Naval Jr. PC. Cascaded deep network systems with linked ensemble components for underwater fish detection in the wild. Ecological Informatics 2019; 52: 103–121. doi: 10.1016/j.ecoinf.2019.05.004 Marini S, Corgnati L, Mantovani C, et al. Automated estimate of fish abundance through the autonomous imaging device GUARD1. Measurement 2018; 126: 72–75. doi: 10.1016/j.measurement.2018.05.035 Salman A, Siddiqui SA, Shafait F, et al. Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES Journal of Marine Science 2020; 77(4): 1295–1307. doi: 10.1093/icesjms/fsz025 Siddiqui SA, Salman A, Malik MI, et al. Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data. ICES Journal of Marine Science 2018; 75(1): 374–389. doi: 10.1093/icesjms/fsx109 Villon S, Mouillot D, Chaumont M, et al. A deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecological Informatics 2018; 48: 238–244. doi: 10.1016/j.ecoinf.2018.09.007 Khatun MA, Baten MA, Farukh MA, Faruk MdO. The impact of climate change on ecosystem services and socio-economic conditions of Char Dwellers in Northern Regions of Bangladesh. Journal of Governance and Accountability Studies 2022; 2(1): 29–48. doi: 10.35912/jgas.v2i1.618 Pulicherla KK, Adapa V, Ghosh M, Ingle P. Current efforts on sustainable green growth in the manufacturing sector to complement “make in India” for making “self-reliant India”. Environmental Research 2022; 206: 112263. doi: 10.1016/j.envres.2021.112263 Nishant R, Kennedy M, Corbett J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management 2020; 53: 102104. doi: 10.1016/j.ijinfomgt.2020.102104 Bibri SE, Krogstie J, Kaboli A, Alahi A. Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology 2024; 19: 100330. doi: 10.1016/j.ese.2023.100330 Lotfian M, Ingensand J, Brovelli MA. The partnership of citizen science and machine learning: Benefits, risks, and future challenges for engagement, data collection, and data quality. Sustainability 2021; 13(14): 8087. doi: 10.3390/su13148087 Pandey DK, Hunjra AI, Bhaskar R, Al-Faryan MAS. Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022. Resources Policy 2023; 86: 104250. doi: 10.1016/j.resourpol.2023.104250 Konya A, Nematzadeh P. Recent applications of AI to environmental disciplines: A review. Science of The Total Environment 2024; 906: 167705. doi: 10.1016/j.scitotenv.2023.167705 Zhang X, Chan FT, Yan C, Bose I. Towards risk-aware artificial intelligence and machine learning systems: An overview. Decision Support Systems 2022; 159: 113800. doi: 10.1016/j.dss.2022.113800 Nicora G, Rios M, Abu-Hanna A, Bellazzi R. Evaluating pointwise reliability of machine learning prediction. Journal of Biomedical Informatics 2022; 127: 103996. doi: 10.1016/j.jbi.2022.103996 Elenchezhian MRP, Vadlamudi V, Raihan R, et al. Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—A review. Smart Materials and Structures 2021; 30(8): 083001. doi: 10.1088/1361-665X/ac099f



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