Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control

Chang, Nan Shan; Mohiuddin, G.; Crawford, Anthony J.; Bai, Kaixu; Jin, Kang-Ren

doi:10.1016/j.ecoinf.2015.05.001

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…Artificial intelligence (AI) applications in aquatic and marine biodiversity and water resource optimize the conservation of aquatic and marine flora and fauna and water resources and attracted significant reserch attention since last decade. For instance, AI and ML models have been used to predict stream flow [98][99][100][101][102][103], water quality , water pollution and toxicology [126][127][128][129][130][131][132], aquatic and marine biodiversity diversity prediction and extinction [133][134][135][136][137][138][139][140][141][142][143][144][145][146][147][148][149], predicting species distribution and habitat mapping [150][151][152][153][154][155][156][157][158][159][160][161][162][163][164], and marine and aquatic species recognition and classification…”

Section: Aquatic and Marine Biodiversity And Water Resource Conservationmentioning

confidence: 99%

“…Out of 34 global biodiversity "hot spots," four are located in India, namely, the eastern and north-eastern Himalayas, Indo-Burma (North-east India), Sundaland, and Western Ghats [186]. Being one of the 17 megadiverse countries, with only 2.4% of global land area, India accounts for 7 to 8% of recorded species in the world [98]. In addition, Indian forests provide many ecosystem services and livelihoods to people.…”

Section: Status Of Indian Forests and Need Of Ai Technologymentioning

confidence: 99%

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Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

et al. 2022

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The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest management.

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Section: Aquatic and Marine Biodiversity And Water Resource Conservationmentioning

confidence: 99%

Section: Status Of Indian Forests and Need Of Ai Technologymentioning

confidence: 99%

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

et al. 2022

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“…Meanwhile, various data-driven models have been used to predict the purification capacity of CWs [ 15 ]. Although a model requires a number of data points as an mechanistic or mathematical model, the data-driven method does not require detailed fundamental and mechanistic knowledge.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based prediction of effluent quality of a constructed wetland

Yang

Xiao

Meng³

et al. 2023

Environmental Science and Ecotechnology

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“…Plusieurs variantes de RNA existent, dont le plus connu est le perceptron multicouche (PMC). La capacité de ce type de RNA à modéliser les problèmes aquatiques n'est plus à démontrer, particulièrement l'eutrophisation (CHANG et al, 2015;HUANG et GAO, 2017;HUO et al, 2013;MA et al, 2014;MULLER et MULLER, 2015;SHOULIANG et al, 2013). Le PMC se caractérise par sa typologie et son fonctionnement.…”

Section: Introductionunclassified

Modélisation de l'évolution spatiotemporelle du phosphore minéral dans une baie lagunaire hypereutrophe tropicale : la baie lagunaire de Tiagba (Côte d'Ivoire)

Yao

Akmel

Akpetou

et al. 2018

rseau

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Ce travail décrit une nouvelle approche de la prédiction de l'évolution spatio-temporelle du phosphore minéral dans les eaux de surface, particulièrement dans la baie lagunaire de Tiagba. L'originalité de cette étude réside dans l'utilisation des réseaux de neurones artificiels, précisément du perceptron multicouche, comme outil de modélisation. Deux approches de l'évolution spatio-temporelle de ce nutriment dans cette baie ont été étudiées : sa modélisation statique et sa modélisation dynamique. Ainsi, il a été utilisé deux bases de 3 966 et 4 627 données respectivement pour sa modélisation statique et sa modélisation dynamique. L'algorithme de Levenberg-Marquardt a été utilisé pour la détermination des poids de connexions lors du développement du perceptron multicouche. Il ressort, des résultats obtenus, que les modèles 5-14-1 et 6-14-2 permettent de prédire à 70,30 % et à environ 70 % respectivement les évolutions statique et dynamique du phosphore minéral dans cette baie lagunaire. Ces modèles, jugés satisfaisant peuvent servir de socle pour d'éventuelles études visant à la réhabilitation et la gestion de cet écosystème aquatique dans le cadre de son développement durable.

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Diagnosis of the artificial intelligence-based predictions of flow regime in a constructed wetland for stormwater pollution control

Cited by 15 publications

References 30 publications

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

Deep learning-based prediction of effluent quality of a constructed wetland

Modélisation de l'évolution spatiotemporelle du phosphore minéral dans une baie lagunaire hypereutrophe tropicale : la baie lagunaire de Tiagba (Côte d'Ivoire)

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