Classificação de estágios sucessionais de Florestas Atlânticas: uma abordagem metodológica baseada em Sistema Fuzzy

Mota, Maurício Tavares da; Bressane, Adriano; Roveda, José Arnaldo Frutuoso; Roveda, Sandra Regina Monteiro Masalskiene

doi:10.5902/1980509830688

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The inadequateness of legislation enforcement has been pointed out to pose a threat to biodiversity and conservation of Campo Rupestre (Miola et al 2019). Moreover, a fuzzy modeling was proposed as an alternative approach to identify the successional stage of São Paulo secondary forests (under Resolução Conama n° 01/1994) in order to mitigate the subjectivity and uncertainty of forest surveys in deforestation requests (Mota et al 2019). Siminski & Fantini (2004) evaluated Resolução Conama n° 04/1994 (Santa Catarina State).…”

Section: Fabio Mostacato Bastos Maurício Jorge Bueno Faria and André ...mentioning

confidence: 99%

Inclusion criteria influence species richness, diversity and forest structure with implications to deforestation permits in Rio de Janeiro State, Brazil

Bastos¹,

Faria

Nunes-Freitas

2023

Acta Bot. Bras.

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Deforestation requests to government decision-makers aim for the legal permission to remove secondary forests for economic reasons. In the State of Rio de Janeiro, Resolução Conama nº 06/1994 drives the analysis of successional stages of such forests. It does not define which inclusion criterion (diameter at breast height) should be used in forest inventories. We used Floresta da Cicuta as a case study to evaluate the influence of different inclusion criteria on species richness, diversity and structure parameters having Resolução Conama nº 06/1994 as background. Using Hill numbers, we found that the DBH ≥ 2.5 cm highlighted species richness. Species richness is improperly addressed in Resolução Conama nº 06/1994. The use of DBH ≥ 2.5 cm detected 30 individuals of threatened species, while DBH ≥ 10 cm detected only nine. Basal area was the most accurate parameter to identify the successional stage. Mean DBH and mean height misidentified successional stage. We strongly advise environmental authorities to demand the use of DBH ≥ 2.5 cm in forest inventories for deforestation requests. We strongly encourage the review of Resolução Conama nº 06/1994 by environmental authorities in order to reduce the risk of mistaken classification of successional stage of secondary forests.

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Section: Fabio Mostacato Bastos Maurício Jorge Bueno Faria and André ...mentioning

confidence: 99%

Inclusion criteria influence species richness, diversity and forest structure with implications to deforestation permits in Rio de Janeiro State, Brazil

Bastos¹,

Faria

Nunes-Freitas

2023

Acta Bot. Bras.

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“…Fuzzy logic allows for the representation and processing of these uncertainties by employing linguistic variables, membership functions, and fuzzy rules [44][45][46][47][48]. It enables a more nuanced approach to decision-making, accommodating imprecise data and providing a degree of flexibility that traditional binary logic may not offer [49][50][51][52][53]. This is crucial in situations where the suitability of landfill sites is influenced by complex, interrelated factors, and where precise, deterministic models may fall short in capturing the full spectrum of uncertainty and variability present in the data.…”

Section: Distance From Gas and Oil Pipelinesmentioning

confidence: 99%

Enhancing Landfill Monitoring and Assessment: A Proposal Combining GIS-Based Analytic Hierarchy Processes and Fuzzy Artificial Intelligence

Loureiro,

Bressane,

Nascimento

et al. 2023

Knowledge

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The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment.

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“…FISs are computational frameworks based on fuzzy set theory, introduced by [1], which allow for reasoning about data that are uncertain or imprecise. FISs utilize a set of fuzzy rules and membership functions to model complex systems, offering a transparent and interpretable approach to handle uncertainty effectively [2]. This study explores a critical question: is the capability of FISs to address uncertainty indeed pivotal for environmental engineering applications?…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?

Bressane,

Garcia,

Castro

et al. 2024

Sustainability

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Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.

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Classificação de estágios sucessionais de Florestas Atlânticas: uma abordagem metodológica baseada em Sistema Fuzzy

Cited by 7 publications

References 6 publications

Inclusion criteria influence species richness, diversity and forest structure with implications to deforestation permits in Rio de Janeiro State, Brazil

Inclusion criteria influence species richness, diversity and forest structure with implications to deforestation permits in Rio de Janeiro State, Brazil

Enhancing Landfill Monitoring and Assessment: A Proposal Combining GIS-Based Analytic Hierarchy Processes and Fuzzy Artificial Intelligence

Fuzzy Machine Learning Applications in Environmental Engineering: Does the Ability to Deal with Uncertainty Really Matter?

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