Evaluation of the factors explaining the use of agricultural land: A machine learning and model-agnostic approach

Viana, Cláudia M.; Santos, M. Isabel Reis dos; Freire, Dulce; Abrantes, Patrícia; Rocha, Jorge

doi:10.1016/j.ecolind.2021.108200

Cited by 39 publications

(15 citation statements)

References 61 publications

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“…To thoroughly assess the variables that can potentially explain why agricultural land is used for plantations of wheat, maize, and olive trees, Viana et al [243] implemented an ML and agnostic-model approach to show global and local explanations of the most important variables. Machine Learning model Random Forest and XAI approach LIME were deployed for analysis and approximately 140 variables related to agricultural socioeconomic, biophysical, and bioclimatic factors were gathered.…”

Section: ) Xai For Cyber Security Of Smart Farmingmentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internetconnected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning (ML) and Deep Learning (DL) has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most ML-based techniques and DL-based techniques are deployed in the ''black-box'' manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users' confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security.

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Section: ) Xai For Cyber Security Of Smart Farmingmentioning

confidence: 99%

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

et al. 2022

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“…O Machine Learning (ML) supera com sucesso as limitações dos métodos estatísticos. Apresenta vantagens, tais como a capacidade de lidar com dados de diferentes tipos, estruturas e quantidades (i.e., big data), não sendo sensíveis à escala das variáveis (o que significa que não há necessidade de normalização da variável) (Viana et al, 2021); por conseguinte, é possível combinar dados de diversas fontes para modelar relações não lineares complexas que descrevam a variação do sentimento em ambientes urbanos.…”

Section: Metodologiaunclassified

Desigualdades em saúde, desigualdades no território: desafios para os países de língua portuguesa em contexto de pós pandemia

2022

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“…For example, fuzzy logic-based methods may produce overfitted models because their rules are based on heuristics, although they can model uncertainty in real world data with continuous boundaries [22,23]. Moreover, these traditional methods may not work well when modeling complex nonlinear relationships between potential driving factors and land use, and the effects of heterogeneity may be discarded or not fully captured, especially for the process of urban development [10,20,24]. Some traditional methods have the limitation of being black-box models, and the statistical significance of the drivers for LUCC is obscured, which may not be conducive to watershed management [10,22].…”

Section: Introductionmentioning

confidence: 99%

A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS

et al. 2023

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Understanding the land use/cover change (LUCC) in watersheds is essential for sustainable development. The CA-Markov model has been proven to be an effective method for land use modeling because of its simplicity and potential for evolution. However, it is difficult to apply this method to meet the requirement of land use planning and management since it fails to consider the driving forces of LUCC. To evaluate the factors that influence LUCC comprehensively, we developed and implemented a machine learning-based CA-Markov model to understand the dynamics of LUCC in a coastal watershed in Southeast China, the Minjiang River Watershed (MRW). The proposed method performed well for each land use category, with average AUC values of 0.999 and 0.916 for the training and testing periods, respectively, for suitable images. The overall accuracy for LUCC was 0.971. The urbanization process in the MRW was speeding up recently. Urban area increased by 2.22% of the total area during 2015–2020, and most of that was from conversion of woodland and agricultural land. Additionally, the proposed method provided a much deeper understating of the forces driving the LUCC on a regional scale. Population and gross domestic product (GDP) were the major factors influencing the distribution of urbanized land in the MRW. In contrast, woodland distribution was highly related to topographic factors in the MRW. Scenario analysis was also employed to identify patterns of LUCC under different scenarios. The results showed that the process of urbanization may become more complex with increasing population and GDP and that land use evolution may be more sustainable with scientific spatial plans which consider facilities for people and ecological protection. The proposed method quantifies the LUCC in changing environmental settings and can serve as a helpful tool for sustainable watershed management.

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Evaluation of the factors explaining the use of agricultural land: A machine learning and model-agnostic approach

Cited by 39 publications

References 61 publications

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Desigualdades em saúde, desigualdades no território: desafios para os países de língua portuguesa em contexto de pós pandemia

A Random Forest-Based CA-Markov Model to Examine the Dynamics of Land Use/Cover Change Aided with Remote Sensing and GIS

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