scholarly journals A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2444 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ebrahim Omidavr ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Slope Angle ◽  
Area Under The Curve ◽  
Kernel Functions ◽  
Gully Erosion ◽  
Support Vector ◽  
Base Function ◽  
Conditioning Factors ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

Social media users and cybersecurity awareness: predicting self-disclosure using a hybrid artificial intelligence approach

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naurin Farooq Khan ◽  
Naveed Ikram ◽  
Hajra Murtaza ◽  
Muhammad Aslam Asadi
Keyword(s):  
Artificial Intelligence ◽  
Social Networking Sites ◽  
Hybrid Approach ◽  
Area Under The Curve ◽  
Protective Behavior ◽  
Self Disclosure ◽  
Content Type ◽  
Privacy Risks ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

PurposeThis study aims to investigate the cybersecurity awareness manifested as protective behavior to explain self-disclosure in social networking sites. The disclosure of information about oneself is associated with benefits as well as privacy risks. The individuals self-disclose to gain social capital and display protective behaviors to evade privacy risks by careful cost-benefit calculation of disclosing information.Design/methodology/approachThis study explores the role of cyber protection behavior in predicting self-disclosure along with demographics (age and gender) and digital divide (frequency of Internet access) variables by conducting a face-to-face survey. Data were collected from 284 participants. The model is validated by using multiple hierarchal regression along with the artificial intelligence approach.FindingsThe results revealed that cyber protection behavior significantly explains the variance in self-disclosure behavior. The complementary use of five machine learning (ML) algorithms further validated the model. The ML algorithms predicted self-disclosure with an area under the curve of 0.74 and an F1 measure of 0.70.Practical implicationsThe findings suggest that costs associated with self-disclosure can be mitigated by educating the individuals to heighten their cybersecurity awareness through cybersecurity training programs.Originality/valueThis study uses a hybrid approach to assess the influence of cyber protection behavior on self-disclosure using expectant valence theory (EVT).

scholarly journals Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

2020 ◽  
Vol 12 (21) ◽  
pp. 3620
Author(s):  
Indrajit Chowdhuri ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Land Use Changes ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Gully Erosion ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Chotanagpur Plateau ◽  
Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

scholarly journals An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hong-Hai Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Soil Improvement ◽  
Function Parameter ◽  
Support Vector ◽  
Established Method ◽  
In Situ Data ◽  
Svm Algorithm ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and  failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

scholarly journals AN ARTIFICIAL INTELLIGENCE APPROACH TO PREDICTION OF EXTREME EVENTS: THE CASE OF STORMS IN WESTERN FRANCE

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 115-122
Author(s):  
A. Frifra ◽  
M. Maanan ◽  
H. Rhinane ◽  
M. Maanan
Keyword(s):  
Artificial Intelligence ◽  
Extreme Events ◽  
Human Life ◽  
Low Frequency ◽  
Support Vector ◽  
Storm Characteristics ◽  
Buoy Data ◽  
Gated Recurrent Unit ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.

Sign in / Sign up

Export Citation Format

Share Document