An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Tunusluoglu, M. C.; Gökçeoğlu, Candan; Sönmez, H.; Nefeslioğlu, Hakan A.

doi:10.5194/nhess-7-557-2007

Cited by 19 publications

(4 citation statements)

References 54 publications

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“…Furthermore, there are two different approaches: error back-propagation algorithms, a gradient method, and a genetic algorithm, which is a stochastic search method [35]. The back-propagation training algorithm is the most frequently used neural network method [36][37][38]. It is trained using a set of examples of associated input and output values.…”

Section: Basic Principle Of Annmentioning

confidence: 99%

Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

Kim

Lee

2020

Applied Sciences

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Subsidence at abandoned mines sometimes causes destruction of local areas and casualties. This paper proposes a mine subsidence risk index and establishes a subsidence risk grade based on two separate analyses of A and B to predict the occurrence of subsidence at an abandoned mine. For the analyses, 227 locations were ultimately selected at 15 abandoned coal mines and 22 abandoned mines of other types (i.e., gold, silver, and metal mines). Analysis A predicts whether subsidence is likely using an artificial neural network. Analysis B assesses a mine subsidence risk index that indicates the extent of risk of subsidence. Results of both analyses are utilized to assign a subsidence risk grade to each ground location investigated. To check the model's reliability, a new dataset of 22 locations was selected from five other abandoned mines; the subsidence risk grade results were compared with those of the actual ground conditions. The resulting correct prediction percentage for 13 subsidence locations of the abandoned mines was 83-86%. To improve reliability of the subsidence risk, much more subsidence data with greater variations in ground conditions is required, and various types of analyses by numerical and empirical approaches, etc. need to be combined. ANN provides tools for optimising operations, equipment selection, and problems involving large amounts of information that humans cannot easily assimilate in the process of decision-making. Hence, the neural network can serve as a tool for determining the relative importance of the factors influencing the stability of underground objects [18].Since the 1990s, when a number of ANN-based systems were introduced in the mining industry [19], ANN has significantly improved the current approach towards predicting abandoned mine subsidence risk [20][21][22][23]. In particular, Oh et al. [24] evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility maps. Kim et al. [25] also constructed a hazard map for possible ground subsidence around abandoned underground coal mines in Korea using an ANN together with a geographic information system (GIS). Zhao and Chen [26] used an ANN to predict ground subsidence at a metal mine because ground subsidence is influenced by many factors which are fuzzy and nonlinear, and the ANN has a great function to be able to handle such nonlinear problems, without knowing specific mine conditions. Meanwhile, Mine Reclamation Corporation (MIRECO) who is responsible for the safety and management of abandoned mines and implements mine reclamation projects in Korea has made several attempts to reliably predict subsidence by numerical, statistical, probabilistic, and/or model approaches [27-31] prior to this study. However, the prediction results were not up to par with MIRECO's standards because they were not appropriate for the assessment of many as well as extensive abandoned mines.Finally, MIRECO decided to adopt ANN as well to predict subsidence consideri...

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Section: Basic Principle Of Annmentioning

confidence: 99%

Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

Kim

Lee

2020

Applied Sciences

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“…The momentum coefficient was set to 0.9. Additionally, a dynamic learning rate procedure published by Tunusluoglu et al (2007) was employed. In order to acquire appropriate numbers of the neurons in the hidden layer the heuristic proposed by Wang (1994) were implemented.…”

Section: Topographic Attributesmentioning

confidence: 99%

An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping

Nefeslioğlu¹,

San²,

Gökçeoğlu

et al. 2012

International Journal of Applied Earth Observation and Geoinfor

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“…In addition, Jacobs (1988) proposed a procedure to apply a dynamic learning rate in the training stages. Considering the suggestions, in this study, a dynamic learning rate procedure published by Tunusluoglu et al (2007) was employed and the computer code written by Sonmez et al (2006) was implemented. The last stage in the construction of a neural network is the determination of appropriate numbers of hidden layers and of neurons in these layers.…”

Section: Shallow Landslide Initiation Susceptibilitymentioning

confidence: 99%

Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey)

et al. 2011

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The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955-2007 were recognised using different data sources. A 'Sampling Circle' approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100years were produced.

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An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey)

Cited by 19 publications

References 54 publications

Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping

Medium-scale hazard mapping for shallow landslide initiation: the Buyukkoy catchment area (Cayeli, Rize, Turkey)

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