A GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling

Tafreshi, Ghazaleh Mohebbi; Nakhaei, Mohammad; Lak, Razyeh

doi:10.1007/s00477-020-01810-3

Cited by 20 publications

(9 citation statements)

References 97 publications

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“…The spatial and temporal land deformation is measured through InSAR observations and it is a microwave remote sensing system (Orhan et al, 2021). Several machine learning algorithm (MLA) has been used over time to predict LSSM, such as logistic regression (LR) (Tien Bui et al, 2018), artificial neural network (ANN) (Mohebbi Tafreshi et al, 2020), support vector machine (SVM) (Arabameri et al, 2021), logistic tree model (LTM) (Arabameri et al, 2021), decision tree (DT) (Lee and Park, 2013) and so on. However, the most recent ensemble model, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, four popular MLAs namely the Maximum Entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM) were used for modeling and mapping of LS, based on the stateof-the-art skillful characteristics and literature study (Abdollahi et al, 2019). The selection behind these MLAs are based on their earlier involvement in different research work on natural hazard susceptibility studies and respective optimal prediction performance (Zamanirad et al, 2019;Mohebbi Tafreshi et al, 2020;Najafi et al, 2020). In the case of MaxEnt, it has the ability to choose the correct estimation of the uncertain probability distribution and to select the highest entropy of the given probabilistic constraints.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…There are many fuzzy membership functions in the fuzzy algorithm of ArcGIS software v.10.5 that produce a sigmoid pattern out of the membership, which is used in most of the fuzzy logic applications (Mohebbi Tafreshi et al, 2020; Raines et al, 2010). Each function is applied based on the parameters’ midpoint and spread.…”

Section: Methodsmentioning

confidence: 99%

A comparison of fuzzy logic and TOPSIS methods for landfill site selection according to field visits, engineering geology approach and geotechnical experiments (case study: Rudbar County, Iran)

Chamchali

Ghazifard

2020

Waste Manag Res

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The present study evaluated and selected the best location among susceptible landfill sites in Rudbar County using 27 criteria, as the maximum effective criteria, in the decision-making process. The emergence and comparison between the two methods of fuzzy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with an engineering–geological approach have been conducted to obtain the best results. In this regard, inappropriate regions for landfill construction have been restricted after the conflation of two maps based on the restriction criteria. Then, the direction of prevailing winds and proper area for landfill construction was investigated for a 20-year perspective, and the regions that had more than 35-hectare area and were not located in the direction of the prevailing winds have been identified as responsive sites for landfill construction. Ultimately, selected susceptible regions were prioritized separately based on the TOPSIS and fuzzy methods. To ascertain the accuracy of the results and the reality of the resulted models, complementary field studies, geotechnical digging, and engineering–geological experiments have been used as verification parameters. According to the results of the verification, the fuzzy model having maximum matches with the verified data has been suggested for prioritizing susceptible regions.

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“…• DEM: DEM is a visual and mathematical representation of the height values, related to the mean level, which can reflect surface characteristics, such as slope (Tafreshi et al, 2020, Galeana-Pérez et al, 2021.…”

Section: Land Subsidence Knowledge Graph Constructionmentioning

confidence: 99%

Land Subsidence Prediction Through Modeling of Temporal Attribute Prediction of Knowledge Graph

Lei

Song

Fan

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Land subsidence is a geological disaster. It will lead to the decline of land elevation, resulting in the potential safety hazards of urban facilities. Thus, the prediction of land subsidence displacement is significant. Among the existing prediction methods, the methods based on the time-series prediction model only analyze the settlement series data without considering the settlement mechanism, so they are easy to apply. However, they less consider the influence of other factors on land subsidence. Besides, they independently input displacement time-series data from different monitoring points without considering their relationship. To solve these problems, we take the monitoring point as the entity and take the DEM, soil type, building height, and land subsidence displacement sequence at the corresponding position of the monitoring point as the attributes to construct the knowledge graph. And then, we propose a framework Graph-TAP for modeling temporal attribute prediction of the knowledge graph’s entity. This framework learns the representation of events with the specific entity at first. Then it captures the temporal dependency between historical events using GRU. Finally, it predicts the entity’s displacement attribute. We randomly selected 61 subsidence monitoring points in Shenzhen, China. We used the land subsidence displacement InSAR time-series data (12-day time interval) and other attribute data from June 22, 2015, to April 5, 2016, for model training, validation, and testing. The experimental results show that our method is better than the time-series prediction based on the LSTM model and the DARNET (a knowledge graph temporal attribute prediction framework).

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A GIS-based comparative study of hybrid fuzzy-gene expression programming and hybrid fuzzy-artificial neural network for land subsidence susceptibility modeling

Cited by 20 publications

References 97 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

A comparison of fuzzy logic and TOPSIS methods for landfill site selection according to field visits, engineering geology approach and geotechnical experiments (case study: Rudbar County, Iran)

Land Subsidence Prediction Through Modeling of Temporal Attribute Prediction of Knowledge Graph

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