Large-scale prediction of tropical stream water quality using Rough Sets Theory

Albuquerque, Laysson Guillen; Roque, Fábio de Oliveira; Valente‐Neto, Francisco; Koroiva, None Ricardo; Buss, Daniel Forsin; Baptista, Darcílio Fernandes; Hepp, Luiz Ubiratan; Kuhlmann, Mônica Luisa; Sundar, Shyam; Covich, Alan P.; Pinto, J.O.P.

doi:10.1016/j.ecoinf.2021.101226

Cited by 12 publications

(2 citation statements)

References 60 publications

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“…Green energy investment is an economic behavior and complex management process, as it is not easy to achieve both ecological goals and economic benefits. Green energy investment is a novel resource allocation strategy that allocates limited resources toward the advancement of renewable resources and green technology [31]; however, only when green technology is profitable would firms be pushed to make green investments [32]. The interest in green energy investment among academics and practitioners has increased due to its promising impact on environmental improvements that support the ecological environment [33].…”

Section: Green Investment In the Big Data Eramentioning

confidence: 99%

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Huang,

Liang,

Chuang

et al. 2024

Sustainability

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The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets.

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Section: Green Investment In the Big Data Eramentioning

confidence: 99%

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Huang,

Liang,

Chuang

et al. 2024

Sustainability

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“…To date, only very few limited papers have reported on RSML for biomass pyrolysis, such as its BC energy potential (Tang et al 2023), BC surface properties , bio-oil properties (Chong et al 2022). The other studies using RSML include forecasting CO 2 storage integrity (Aviso et al 2019), building energy usage (Lei et al 2021), detection of city energy usage and greenhouse gaseous (GHG) emissions (Aviso et al 2021), estimating water quality (Albuquerque et al 2021), forecasting impeller service life (Zhao et al 2019), and evaluating variable effectiveness and success criteria for construction projects (Akbari et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Predictive capability of rough set machine learning in tetracycline adsorption using biochar

Balasubramanian,

Prabhakar,

Liu

et al. 2024

Carbon Res.

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Machine learning algorithms investigate relationships in data to deliver useful outputs. However, past models required complete datasets as a prerequisite. In this study, rough set-based machine learning was applied using real-world incomplete datasets to generate a prediction model of biochar’s adsorption capacity based on key attributes. The predictive model consists of if–then rules classifying properties by fulfilling certain conditions. The rules generated from both complete and incomplete datasets exhibit high certainty and coverage, along with scientific coherence. Based on the complete dataset model, optimal pyrolysis conditions, biomass characteristics and adsorption conditions were identified to maximize tetracycline adsorption capacity (> 200 mg/g) by biochar. This study demonstrates the capabilities of rough set-based machine learning using incomplete practical real-world data without compromising key features. The approach can generate valid predictive models even with missing values in datasets. Overall, the preliminary results show promise for applying rough set machine learning to real-world, incomplete data for generating biomass and biochar predictive models. However, further refinement and testing are warranted before practical implementation.

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Effects of Agricultural Expansion on Lotic Benthic Macroinvertebrate Communities: A Review and Case Study from Brazil

Corrêa¹,

Utz²

2023

Neotropical Gradients and Their Analysis

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Large-scale prediction of tropical stream water quality using Rough Sets Theory

Cited by 12 publications

References 60 publications

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

Predictive capability of rough set machine learning in tetracycline adsorption using biochar

Effects of Agricultural Expansion on Lotic Benthic Macroinvertebrate Communities: A Review and Case Study from Brazil

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