Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

Chen, Jie‐Jie; Zeng, Zhigang; Jiang, Ping; Tang, Huiming

doi:10.1007/s00521-015-1976-y

Cited by 13 publications

(9 citation statements)

References 49 publications

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“…In practice, the establishment of a susceptibility map is the most important step in landslide hazard management [18,24]. This is because this map is an effective tool to quantify the level of landslide hazard reflected by the spatial likelihood of the landslide occurrences with respect to various conditions of topography, geography, vegetation, climate, and land-use [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

et al. 2018

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This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas.

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Section: Introductionmentioning

confidence: 99%

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

et al. 2018

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“…Also, many researchers work on modifications of EAs to improve their computational performance. In many recently published papers, we can find modifications of GA [22,23,115,116], GP [31,32,117,118], DE [48,49,119,120], ES [64,65], and EP [74,75]. An interesting domain of future research in EAs is also memetic algorithms.…”

Section: Summary and Future Trendsmentioning

confidence: 99%

Evolutionary algorithms and their applications to engineering problems

Słowik

Kwaśnicka

2020

Neural Comput & Applic

405

107

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The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

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“…LSSVM allow to estimate periodic term, while DES calculates the trend component. Chen et al [34] evaluated an approach based on Multi Genetic Programming (MGP) by using Separable Functional Network (SFN). However, this method depends of the choice of suitable parameters for Multi-Gene Genetic Programming (MGGP) and the selection of an appropriate structure of Functional Networks (FNs).…”

Section: Neural Network To Predict Landslides' Displacementmentioning

confidence: 99%

Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

JUSPN

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Landslides are phenomena that cause significant human and economic losses. Researchers have investigated the prediction of high landslides susceptibility with various methodologies based upon statistical and mathematical models, in addition to artificial intelligence tools. These methodologies allow to determine the areas that could present a serious risk of landslides. Monitoring these risky areas is particularly important for developing an Early Warning Systems (EWS). As matter of fact, the variety of landslides’ types make their monitoring a sophisticated task to accomplish. Indeed, each landslide area has its own specificities and potential triggering factors; therefore, there is no single device that can monitor all types of landslides. Consequently, Wireless Sensor Networks (WSN) combined with Internet of Things (IoT) allow to set up large-scale data acquisition systems. In addition, recent advances in Artificial Intelligence (AI) and Federated Learning (FL) allow to develop performant algorithms to analyze this data and predict early landslides events at edge level (on gateways). These algorithms are trained in this case at fog level on specific hardware. The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models.

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Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

Cited by 13 publications

References 49 publications

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

Evolutionary algorithms and their applications to engineering problems

Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**

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