A forecasting method of forest pests based on the rough set and PSO-BP neural network

Bai, Tiecheng; Meng, Hao-Tian; Yao, Jianghe

doi:10.1007/s00521-014-1658-1

Cited by 32 publications

(14 citation statements)

References 12 publications

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“…They then introduced this set to CCANN models in which the Kalman learning algorithm was embedded for training. Bai et al (2014) applied rough set theory to eliminate redundancy attributes, for which input factors D r a f t could be reduced from 16 to eight. Following this, Bai et al (2014) used a PSO algorithm to optimize weights and thresholds in BPNN.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…For example, using modern remote sensing and mapping techniques, ML methods effectively improved the accuracy of species distribution models (SDMs) (Garzón et D r a f t Vaca et al 2011;Pouteau et al 2012;Faleiro et al 2013;Périé and Blois 2016), or in combination with traditional processes or empirical models, they were used to predict carbon (C) and energy fluxes (Papale and Valentini 2003;Papale et al 2015;Cropper 2008, 2010;Tramontana et al 2015Tramontana et al , 2016. ML methods were also used in hazard assessment and forest management (Rogan et al 2008;Hlásny et al 2011;Hlásny and Turčáni 2013;Fassnacht et al 2014;Bai et al 2014;Satir et al 2016;Vahedi 2016;Hengl et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In view of their findings, rough set theory could effectively discard the characteristic dimension and the new POS-BP model could decrease iteration times, with an average accuracy of 94.8 %. ThoughBai et al (2014) yielded results that supported their methodology for classifying dead trees, their attempt to accurately map different mortality stages was defective. In addition, three remote sensing indicators (temperature/Vegetation Dryness Index, LAI, and canopy water content) were defined byWang et al (2010) and combined with ANNs to predict pest hazards in a larch forest.Self-organizing maps (SOMs) are another type of ANN that has been well established in dealing with high-dimensional data (e.g., gridded meteorological data).…”

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confidence: 99%

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Application of machine-learning methods in forest ecology: recent progress and future challenges

et al. 2018

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Machine learning, an important branch of artificial intelligence, is increasingly being applied in sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning (ML) including decision-tree learning, artificial neural network, and support vector machine and their applications in four different aspects of forest ecology over the last decade. These applications include: (i) species distribution models, (ii) carbon cycles, (iii) hazard assessment and prediction, and (iv) other applications in forest management. Although ML approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of ML technologies is limited by the lack of suitable data and the relatively “higher threshold” of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and ML developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of ML in ecology will become an increasingly attractive tool for ecologists in the face of “big data” and that ecologists will gain access to more types of data such as sound and video in the near future, possibly opening new avenues of research in forest ecology.

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Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Application of machine-learning methods in forest ecology: recent progress and future challenges

et al. 2018

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“…Since the theory of rough set was proposed, many researchers have devoted to attribute reduction problem [13] [14][15] [16]. The rough set approach has already been applied in the management of many issues successfully, including data mining [17] [18], decision-making [3] [4], forecasting [5], machine diagnosis [6], recommendation and filtering [7] [8], personal investment portfolio analysis [9] etc. Rough set based methods often work together with other machine learning methods to boost the machine learning performance [3] Instance-based learning (IL), also called example-based, memory-based, or case-based learning, is able to learn outliers in data well.…”

Section: Introductionmentioning

confidence: 99%

Incremental Knowledge Acquisition for WSD: A Rough Set and IL based Method

Huang¹,

Hao²,

Shen³

et al. 2015

ICST Transactions on Scalable Information Systems)

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Word sense disambiguation (WSD) is one of tricky tasks in natural language processing (NLP) as it needs to take into full account all the complexities of language. Because WSD involves in discovering semantic structures from unstructured text, automatic knowledge acquisition of word sense is profoundly difficult. To acquire knowledge about Chinese multi-sense verbs, we introduce an incremental machine learning method which combines rough set method and instance based learning. First, context of a multi-sense verb is extracted into a table; its sense is annotated by a skilled human and stored in the same table. By this way, decision table is formed, and then rules can be extracted within the framework of attributive value reduction of rough set. Instances not entailed by any rule are treated as outliers. When new instances are added to decision table, only the new added and outliers need to be learned further, thus incremental leaning is fulfilled. Experiments show the scale of decision table can be reduced dramatically by this method without performance decline.

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“…The input of the hidden layer and output layer nodes are the weighted value of the output of the previous layer nodes. The incentive degree of each node is decided by its excitation function [7][8][9].…”

Section: Back Propagation Neural Network Modeling Processmentioning
confidence: 99%

Control and Anticontrol of Chaos for Arrhythmia Based on Impulsive Differential Theory
Miao¹
2015
IJUNESST
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A forecasting method of forest pests based on the rough set and PSO-BP neural network

Cited by 32 publications

References 12 publications

Application of machine-learning methods in forest ecology: recent progress and future challenges

Application of machine-learning methods in forest ecology: recent progress and future challenges

Incremental Knowledge Acquisition for WSD: A Rough Set and IL based Method

Control and Anticontrol of Chaos for Arrhythmia Based on Impulsive Differential Theory

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