Enhancing Wetland Restoration through Machine Learning-Based Decision Support Systems
DOI:
https://doi.org/10.62486/latia202581Keywords:
Wetlands, Wetland Restoration, Machine Learning, Deep Learning, Decision Support SystemsAbstract
Researchers are increasingly employing Machine Learning (ML) and Deep Learning (DL) algorithms to address complex geo-environmental challenges, particularly in predicting risk, susceptibility, and vulnerability to environmental changes. These advanced computational models have shown significant promise in various applications, ranging from natural disaster prediction to environmental monitoring. Despite their growing usage, very few studies have leveraged Machine Learning-Based Decision Support Systems (MLBDSS) to restore the health status of wetland habitats. To our knowledge, there are no comparative analyses between Machine Learning models and traditional Decision Support Systems (DSS) in this specific context. Wetlands play a crucial role in supporting biodiversity, including fish and wildlife populations, while also contributing to improved water quality and providing essential ecosystem services to nearby communities. These services include flood control, carbon sequestration, and water filtration, which are vital for both ecological and human well-being. However, over the past decades, wetland areas, particularly in coastal regions, have faced significant degradation due to anthropogenic pressures, resulting in a substantial reduction of these critical benefits. This ongoing loss poses serious ecological and socio-economic challenges that require immediate and effective intervention. Current wetland assessment and mitigation frameworks often encounter limitations in their practical implementation, despite regulatory advancements aimed at promoting wetland conservation. These shortcomings can lead to delayed project approvals, increased costs, and further loss of valuable ecosystem services. Integrating ML and DSS models into wetland management strategies could provide innovative solutions to overcome these challenges by improving predictive accuracy, optimizing restoration efforts, and enhancing decision-making processes. The development of hybrid models combining ML and DSS approaches may offer a more holistic framework for addressing wetland loss, ultimately contributing to sustainable habitat restoration and conservation efforts.
References
Jiang S, Zheng Y, Wang C, Babovic V. Uncovering flooding mechanisms across the contiguous United States through interpretive deep learning on representative catchments. Water Resour Res. 2022;58(1):e2021WR030185. DOI: https://doi.org/10.1029/2021WR030185
Dar SA, Bhat SU, Rashid I. The Status of Current Knowledge, Distribution, and Conservation Challenges of Wetland Ecosystems in Kashmir Himalaya, India. Wetl Conserv Curr Challenges Futur Strateg. 2021;175–200. DOI: https://doi.org/10.1002/9781119692621.ch10
Chatanga P, Seleteng-Kose L. Montane palustrine wetlands of Lesotho: Vegetation, ecosystem services, current status, threats and conservation. Wetlands. 2021;41(6):67. DOI: https://doi.org/10.1007/s13157-021-01470-1
Masson V, Heldens W, Bocher E, Bonhomme M, Bucher B, Burmeister C, et al. City-descriptive input data for urban climate models: Model requirements, data sources and challenges. Urban Clim. 2020;31:100536. DOI: https://doi.org/10.1016/j.uclim.2019.100536
Bhowmik S. Ecological and economic importance of wetlands and their vulnerability: a review. Res Anthol Ecosyst Conserv Preserv Biodivers. 2022;11–27. DOI: https://doi.org/10.4018/978-1-6684-5678-1.ch002
Spieles DJ. Wetland construction, restoration, and integration: A comparative review. Land. 2022;11(4):554. DOI: https://doi.org/10.3390/land11040554
Clare S, Creed IF. The essential role of wetland restoration practitioners in the science-policy-practice process. Front Ecol Evol. 2022;10:838502. DOI: https://doi.org/10.3389/fevo.2022.838502
Fiseha BM, Setegn SG, Melesse AM, Volpi E, Fiori A. Impact of climate change on the hydrology of upper Tiber River Basin using bias corrected regional climate model. Water Resour Manag. 2014;28:1327–43. DOI: https://doi.org/10.1007/s11269-014-0546-x
Teng J, Jakeman AJ, Vaze J, Croke BFW, Dutta D, Kim S. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environ Model Softw. 2017;90:201–16. DOI: https://doi.org/10.1016/j.envsoft.2017.01.006
Boer MM, Nolan RH, Resco De Dios V, Clarke H, Price OF, Bradstock RA. Changing Weather Extremes Call for Early Warning of Potential for Catastrophic Fire. Earth’s Futur. 2017;5(12):1196–202. DOI: https://doi.org/10.1002/2017EF000657
Naidu S, Sajinkumar KS, Oommen T, Anuja VJ, Samuel RA, Muraleedharan C. Early warning system for shallow landslides using rainfall threshold and slope stability analysis. Geosci Front [Internet]. 2018;9(6):1871–82. Available from: https://doi.org/10.1016/j.gsf.2017.10.008 DOI: https://doi.org/10.1016/j.gsf.2017.10.008
Chapple K, Zuk M. Forewarned: The use of neighborhood early warning systems for gentrification and displacement. Cityscape. 2016;18(3):109–30.
Huang Z, Zhao T, Xu W, Cai H, Wang J, Zhang Y, et al. A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts. J Hydrol. 2022;610:127896. DOI: https://doi.org/10.1016/j.jhydrol.2022.127896
Rogers D, Tsirkunov V. Costs and benefits of early warning systems. Glob Assess rep. 2011;
Ndayiragije JM, Li F. Effectiveness of drought indices in the assessment of different types of droughts, managing and mitigating their effects. Climate. 2022;10(9):125. DOI: https://doi.org/10.3390/cli10090125
FAO. The economic lives of smallholder farmers. FAO, Food Agric Organ United Nations. 2015;39.
Turyasingura B, Alex S, Hirwa H, Mohammed FS. Wetland conservation and management practices in Rubanda District, South-Western Uganda. 2022; DOI: https://doi.org/10.21203/rs.3.rs-1876968/v1
Kingsford RT, Bino G, Finlayson CM, Falster D, Fitzsimons JA, Gawlik DE, et al. Ramsar Wetlands of International Importance–Improving Conservation Outcomes. Front Environ Sci. 2021;9(March):1–6. DOI: https://doi.org/10.3389/fenvs.2021.643367
Bridgewater P, Kim RE. The Ramsar Convention on Wetlands at 50. Nat Ecol Evol [Internet]. 2021;5(3):268–70. Available from: http://dx.doi.org/10.1038/s41559-021-01392-5 DOI: https://doi.org/10.1038/s41559-021-01392-5
Basher R. Global early warning systems for natural hazards: systematic and people-centred. Philos Trans R Soc a Math Phys Eng Sci. 2006;364(1845):2167–82. DOI: https://doi.org/10.1098/rsta.2006.1819
Dakos V, Carpenter SR, van Nes EH, Scheffer M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos Trans R Soc B Biol Sci. 2015;370(1659):20130263. DOI: https://doi.org/10.1098/rstb.2013.0263
Tramblay Y, Ruelland D, Somot S, Bouaicha R, Servat E. High-resolution Med-CORDEX regional climate model simulations for hydrological impact studies: a first evaluation of the ALADIN-Climate model in Morocco. Hydrol Earth Syst Sci. 2013;17(10):3721–39. DOI: https://doi.org/10.5194/hess-17-3721-2013
Ardabili S, Mosavi A, Dehghani M, Várkonyi-Kóczy AR. Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. In: Engineering for Sustainable Future: Selected papers of the 18th International Conference on Global Research and Education Inter-Academia–2019 18. Springer; 2020. p. 52–62. DOI: https://doi.org/10.1007/978-3-030-36841-8_5
Ladi T, Jabalameli S, Sharifi A. Applications of machine learning and deep learning methods for climate change mitigation and adaptation. Environ Plan B Urban Anal City Sci. 2022;49(4):1314–30. DOI: https://doi.org/10.1177/23998083221085281
Mudashiru RB, Sabtu N, Abustan I, Balogun W. Flood hazard mapping methods: A review. J Hydrol. 2021;603:126846. DOI: https://doi.org/10.1016/j.jhydrol.2021.126846
Micheli L, Smestad GP, Bessa JG, Muller M, Fernández EF, Almonacid F. Tracking soiling losses: Assessment, Uncertainty, and challenges in mapping. IEEE J Photovoltaics. 2021;12(1):114–8. DOI: https://doi.org/10.1109/JPHOTOV.2021.3113858
Ley A, Haehnel P, Bormann H. Addressing the challenges of climate scenario-based impact studies in modelling groundwater recharge on small barrier islands at the German North Sea coast. J Hydrol Reg Stud. 2023;50:101578. DOI: https://doi.org/10.1016/j.ejrh.2023.101578
Xing Q, Sun Z Bin, Tao Y, Shang J, Miao S, Xiao C, et al. Projections of future temperature-related cardiovascular mortality under climate change, urbanization and population aging in Beijing, China. Environ Int [Internet]. 2022;163(April):107231. Available from: https://doi.org/10.1016/j.envint.2022.107231 DOI: https://doi.org/10.1016/j.envint.2022.107231
Ferreira PVR, Paffenroth R, Wyglinski AM, Hackett TM, Bilén SG, Reinhart RC, et al. Multiobjective reinforcement learning for cognitive satellite communications using deep neural network ensembles. IEEE J Sel Areas Commun. 2018;36(5):1030–41. DOI: https://doi.org/10.1109/JSAC.2018.2832820
Ferreira V, Barreira AP, Pinto P, Panagopoulos T. Understanding attitudes towards the adoption of nature-based solutions and policy priorities shaped by stakeholders’ awareness of climate change. Environ Sci Policy [Internet]. 2022;131(April 2021):149–59. Available from: https://doi.org/10.1016/j.envsci.2022.02.007 DOI: https://doi.org/10.1016/j.envsci.2022.02.007
Verburg PH, Neumann K, Nol L. Challenges in using land use and land cover data for global change studies. Glob Chang Biol. 2011;17(2):974–89. DOI: https://doi.org/10.1111/j.1365-2486.2010.02307.x
Pendergrass AG, Meehl GA, Pulwarty R, Hobbins M, Hoell A, AghaKouchak A, et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat Clim Chang. 2020;10(3):191–9. DOI: https://doi.org/10.1038/s41558-020-0709-0
Alfieri L, Salamon P, Pappenberger F, Wetterhall F, Thielen J. Operational early warning systems for water-related hazards in Europe. Environ Sci Policy. 2012;21:35–49. DOI: https://doi.org/10.1016/j.envsci.2012.01.008
Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of geographical information systems and remote sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005;47(1):81–96.
Pulwarty RS, Sivakumar MVK. Information systems in a changing climate: Early warnings and drought risk management. Weather Clim Extrem. 2014;3:14–21. DOI: https://doi.org/10.1016/j.wace.2014.03.005
Urlainis A, Ornai D, Levy R, Vilnay O, Shohet IM. Loss and damage assessment in critical infrastructures due to extreme events. Saf Sci. 2022;147:105587. DOI: https://doi.org/10.1016/j.ssci.2021.105587
Svetlana D, Radovan D, Ján D. The economic impact of floods and their importance in different regions of the world with emphasis on Europe. Procedia Econ Financ. 2015;34:649–55. DOI: https://doi.org/10.1016/S2212-5671(15)01681-0
Zeleňáková M, Gaňová L, Purcz P, Horský M, Satrapa L, Blišťan P, et al. Mitigation of the adverse consequences of floods for human life, infrastructure, and the environment. Nat Hazards Rev. 2017;18(4):5017002. DOI: https://doi.org/10.1061/(ASCE)NH.1527-6996.0000255
Koks EE, Rozenberg J, Zorn C, Tariverdi M, Vousdoukas M, Fraser SA, et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat Commun. 2019;10(1):2677. DOI: https://doi.org/10.1038/s41467-019-10442-3
Masese A, Neyole E, Ombachi N. Loss and Damage from Flooding in Lower Nyando Basin, Kisumu County, Kenya. Int J Soc Sci Humanit Res. 2016;4(3):9–22.
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