Machine Learning and Knowledge Extraction to Support Work Safety for Smart Forest Operations

Hoenigsberger, Ferdinand; Saranti, Anna; Angerschmid, Alessa; Retzlaff, Carl Orge; Gollob, Christoph; Witzmann, Sarah; Nothdurft, Arne; Kieseberg, Peter; Holzinger, Andreas; Stampfer, Karl

doi:10.1007/978-3-031-14463-9_23

Cited by 7 publications

(11 citation statements)

References 29 publications

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“…Correspondingly, the MI values for these pairs were 0.25, 0.19, and 0.06, respectively. It is noteworthy that all other pairs exhibited MI values below 0.1 [4]. These findings are consistent with both domain knowledge and previous research.…”

Section: Preprocessingsupporting

confidence: 92%

“…The research presented in this paper extends the preliminary findings of a conference paper [4] and explores new insights based on Actionable Explainable AI [5], thereby exploring novel dimensions and unearthing deeper insights into forest operation safety.…”

Section: Introductionmentioning

confidence: 53%

“…Maximum number of features to consider: "auto" (suggesting that the best split was determined by considering all available input features) • Minimum number of samples required for a split: two After evaluation on the test set utilizing four features-specifically, the individual's age at the time of the accident, the day of the week, the time of day, and the classification as a worker or an employee-the decision tree showed a Mean Squared Error (MSE) of 151,222.99 and a Relative Root Mean Squared Error (RRMSE) of 1.15 [4]. The significance of these input features is consistent with previous research findings [7,19], as illustrated in Figure 3.…”

Section: Decision Treementioning

confidence: 99%

See 2 more Smart Citations

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

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Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers’ Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain.

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Section: Preprocessingsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 53%

Section: Decision Treementioning

confidence: 99%

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Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Hoenigsberger,

Saranti,

Jalali

et al. 2024

Applied Sciences

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“…Although deep learning methods have powerful feature extraction and characterization capabilities that can replace the process of feature construction to some extent, deep learning methods rely on large amounts of labeled data, which is difficult for a limited number of TCSs. In addition, the inexplicability and security risks [31] of deep learning are criticized by researchers in the field of urban and rural planning, which restrict the development of deep learning techniques in the field of environmental pattern research in TCSs. To address these issues, we investigated environmental factors and found that elements in natural environments such as mountains, rivers, farmlands, forests, and vegetation regions play dominant roles in environmental patterns [17][18][19][32][33][34].…”

Section: Classification Study Of Tcssmentioning

confidence: 99%

An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning

Kong

Xue

et al. 2023

Applied Sciences

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The recognition of environmental patterns for traditional Chinese settlements (TCSs) is a crucial task for rural planning. Traditionally, this task primarily relies on manual operations, which are inefficient and time consuming. In this paper, we study the use of deep learning techniques to achieve automatic recognition of environmental patterns in TCSs based on environmental features learned from remote sensing images and digital elevation models. Specifically, due to the lack of available datasets, a new TCS dataset was created featuring five representative environmental patterns. We also use several representative CNNs to benchmark the new dataset, finding that overfitting and geographical discrepancies largely contribute to low classification performance. Consequently, we employ a semantic segmentation model to extract the dominant elements of the input data, utilizing a metric-based meta-learning method to enable the few-shot recognition of TCS samples in new areas by comparing their similarities. Extensive experiments on the newly created dataset validate the effectiveness of our proposed method, indicating a significant improvement in the generalization ability and performance of the baselines. In sum, the proposed method can automatically recognize TCS samples in new areas, providing a powerful and reliable tool for environmental pattern research in TCSs.

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“…A major problem is that the rules do not cover all subsets of the entered attributes, which can lead to classification failure. In certain real-world applications, a wrong decision can lead to life-threatening situations, e.g., in medicine [11], in agriculture [12] or in forestry [13,14]. Therefore, in these application areas, it is necessary to use the cognitive abilities of humans in general, since humans can bring intuitive experiential knowledge to some situations [15].…”

Section: Introductionmentioning

confidence: 99%

Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning

Saranti

Hudec

Mináriková

et al. 2022

MAKE

Self Cite

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In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites. Human domain experts are often able to mark any entity, place it in a different class and adjust the position of the slope in the class. Moreover, they can often explain the classification space linguistically—depending on their individual domain experience and previous knowledge. We consider this human-in-the-loop extremely important and call our approach actionable explainable AI. Consequently, the parameters of the functions are adapted to these requirements and the solution is explained to the domain experts accordingly. Specifically, this paper contains three novelties going beyond the state-of-the-art: (1) A novel method for detecting the appropriate parameter range for the averaging function to treat the slope in the “maybe” class, along with a proposal for a better generalisation than the existing solution. (2) the insight that for a given problem, the family of t-norms and t-conorms covering the whole range of nilpotency is suitable because we need a clear “no” or “yes” not only for the borderline cases. Consequently, we adopted the Schweizer–Sklar family of t-norms or t-conorms in ordinal sums. (3) A new fuzzy quasi-dissimilarity function for classification into three classes: Main difference, irrelevant difference and partial difference. We conducted all of our experiments with real-world datasets.

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Machine Learning and Knowledge Extraction to Support Work Safety for Smart Forest Operations

Cited by 7 publications

References 29 publications

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

Explainable Artificial Intelligence to Support Work Safety in Forestry: Insights from Two Large Datasets, Open Challenges, and Future Work

An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning

Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning

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