Evaluation of the effects of uncertainty on the predictions of landslide occurrences using the Shannon entropy theory and Dempster–Shafer theory

Milaghardan, Amin Hosseinpoor; Abbaspour, Rahim Ali; Khalesian, Mina

doi:10.1007/s11069-019-03798-8

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…For example, uplift creates new geomorphological formations, and over time weathering processes become a trigger for landform collapse, leading to a state of equilibrium and being reduced closer to sea level [38,39]. Meanwhile, "geomorphological entropy" has a connection with the second law of thermodynamical entropy; describing increase in entropy in every natural process providing all systems included in the process are considered [40][41][42][43][44]. The increase in entropy drives losses in energy used for mechanical work.…”

Section: Mining and Natural Resource Utilizationmentioning

confidence: 99%

Quantitative-Qualitative Method for Quick Assessment of Geodiversity

Zakharovskyi

Németh

2021

Land

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The article demonstrates a method for quantitative-qualitative geodiversity assessment based on core elements of abiotic nature (geology and geomorphology) according to a proposed weight multiplied by the area of spread through the studied region. The territory of the Coromandel Peninsula was selected as a case study due to its diverse geology and geomorphology. The north part of the Peninsula (Port Jackson, Fletcher Bay and Port Charles districts) was chosen because of the variety of rock types (sedimentary and volcanic groups) covering the region, while historical stratovolcano remnants and old sediments provide a good variety of meadow hills and weathered coastal cliffs. Meanwhile, the method utilizes easily accessible data (topographical and geological map) to assess slope angle (morphometry) and rock groups, including their age (geology) to identify areas in the sample region with significant geodiversity values. Moreover, the aim of this research is to make the assessment of geodiversity simpler and more accessible for various parts of the world with minimal required information. In this paper, we provide access to improve and utilize this method in geologically diverse territories to select the best areas for geotourism, geoeducation and geconservation planning.

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Section: Mining and Natural Resource Utilizationmentioning

confidence: 99%

Quantitative-Qualitative Method for Quick Assessment of Geodiversity

Zakharovskyi

Németh

2021

Land

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“…The entropy of a system conceptually measures the degree of randomness, disorder, uncertainty, or instability of a system [6,33]. Claude Shannon, in 1948, developed the concept of entropy to analyze a fundamental communication problem of information theory, but later, this theory was found to be helpful in other areas.…”

Section: Shannon Entropy (Se) Modelmentioning

confidence: 99%

“…Landslide susceptibility mapping (LSM) is carried out using various statistical models to determine the frequency and probability of a landslide event [24]. Some commonly used models include frequency ratio (FR) [25][26][27], certainty factor (CF) [28][29][30], Shannon entropy (SE) [31][32][33][34], weight of index (WOI) [35,36], and evidential belief function (EBF) [37,38]. The disadvantages of statistical methods include lower predictive potential, simplifying complex relationships, and human interference during feature selection [8,39].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis

2021

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Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE) model with random forest (RF) and support vector machine (SVM) models. An inventory of 1723 rainfall-induced landslides was generated and randomly selected for training (1199; 70%) and validation (524; 30%) purposes. A set of 14 relevant factors was selected and checked for multicollinearity. These factors were first ranked using Information Gain and Chi-square feature ranking algorithms. Furthermore, Wilcoxon Signed Rank Test and One-Sample T-Test were applied to check their statistical significance. An optimum subset of 11 landslide causative factors was then used for generating landslide susceptibility maps (LSM) using hybrid SE-RF and SE-SVM models. These LSM’s were validated and compared using receiver operating characteristic (ROC) curves and performance matrices. The SE-RF performed better with training and validation accuracies of 96.93% and 88.94%, respectively, compared with the SE-SVM model with training and validation accuracies of 94.05% and 82.4%, respectively. The prediction matrices also confirmed that the SE-RF model is better and is recommended for the landslide susceptibility analysis of similar mountainous regions worldwide.

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“…Wang & Brenning, 2021;Xing et al, 2021), random forest (RF) (Nhu et al, 2020;Sun, Wen, Wang, & Xu, 2020), evidence belief function (EBF) (Althuwaynee et al, 2014;Z. Chen, Liang, Ke, Yang, & Zhao, 2020;Ding et al, 2017;Feby, Achu, Jimnisha, Ayisha, & Reghunath, 2020;Zhang et al, 2016), Dempster-Shafer (W. Chen, Pourghasemi, & Zhao, 2017;Bakhtiar Feizizadeh & Blaschke, 2014;Bakhtiar Feizizadeh, Jankowski, & Blaschke, 2014;Gudiyangada Nachappa, Tavakkoli Piralilou, Ghorbanzadeh, Shahabi, & Blaschke, 2019;Hosseinpoor Milaghardan, Ali Abbaspour, & Khalesian, 2020;Mezaal, Pradhan, & Rizeei, 2018;Mohammady et al, 2012;Pourghasemi, Pradhan, Gokceoglu, & Moezzi, 2013;Shirani, Pasandi, & Arabameri, 2018), decision tree (Wang et al, 2016;Pham, et al 2020, Wu, et al 2020, logistic regression (LR) (Aditian et al, 2018;Althuwaynee et al, 2014;Felicísimo, Cuartero, Remondo, & Quirós, 2013;Pradhan et al, 2008;L.-J. Wang, Guo, Sawada, Lin, & Zhang, 2016),, fuzzy logic (Feizizadeh et al 2014, Kumar and Anbalagan 2015, Abedi Gheshlaghi and Feizizadeh 2017, Bahrami et al 2021, artificial neural networks (Gorsevski et al, 2016;Wang et al, 2016;Aditian et al, 2018 ), and deep learning algorithms (Bui et al 2020, Dao et al 2020, Huang et al 2020…”

Section: Introductionmentioning

confidence: 99%

Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions

Beheshtifar

2022

Nat Hazards

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Landslides are considered to be one of the most significant natural hazards. Detection of landslide-prone zones is an important phase in landslide hazard assessment and mitigation of landslide-related losses. AHP as one of the most effective methods for GIS-based multi-criteria decision analysis is increasingly being used in susceptibility mapping.However, its weights have some degree of uncertainty that interval comparison matrix (ICM) method can be used to deal with this problem. The importance of this study is to propose an interval number distance-based region growing (IDRG) method based on ICM for the identification of landslide-prone zones in the Urmia lake basin, Iran. To assess the capability of the proposed IDRG method, a landslide susceptibility map was produced using common AHP, too.To generate the maps, the weights of nine conditioning factors were determined using both traditional pairwise comparison matrices (PCM) of the AHP method and ICM. The accuracy of the produced maps was assessed through ROC (receiver operating curve) and using a dataset of known landslide occurrences. The results indicate an improvement in accuracy of about 11% by identifying the landslide-prone zones using the IDRG method. This improvement was achieved by minimizing the uncertainty associated with criteria ranking/weighting in a traditional AHP and identifying the prone zones as areas instead of pixels.

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Evaluation of the effects of uncertainty on the predictions of landslide occurrences using the Shannon entropy theory and Dempster–Shafer theory

Cited by 10 publications

References 31 publications

Quantitative-Qualitative Method for Quick Assessment of Geodiversity

Quantitative-Qualitative Method for Quick Assessment of Geodiversity

Entropy-Based Hybrid Integration of Random Forest and Support Vector Machine for Landslide Susceptibility Analysis

Identification of landslide-prone zones using a GIS-based multi-criteria decision analysis and region-growing algorithm in uncertain conditions

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