Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

(2020) Dong Van Dao et al.   Materials

GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

(2020) Binh Thai Pham et al.   Water

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

(2020) Dong Van Dao et al.   Sustainability

Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

(2020) Guirong Wang et al.   Symmetry-Basel

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

(2020) Dong Van Dao et al.   CATENA

Hybridizing four wise neural-metaheuristic paradigms in predicting soil shear strength

(2020) Hossein Moayedi et al.   MEASUREMENT

Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression

(2020) Hung Quang Nguyen et al.   Materials

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

(2020) Viet-Ha Nhu et al.   Forests

Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers

(2020) Binh Thai Pham et al.   Geocarto International

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

(2020) Phong Tung Nguyen et al.   International Journal of Environmental Research and Public Health

A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil

(2020) Binh Thai Pham et al.   Sustainability

Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

(2020) Danial Jahed Armaghani et al.   Sustainability

Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

(2020) Hai-Bang Ly et al.   Sustainability

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

(2020) Binh Thai Pham et al.   Sustainability

Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest

(2020) Tuan Anh Pham et al.   Applied Sciences-Basel

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

(2020) Phong Tung Nguyen et al.   Applied Sciences-Basel

Development of advanced artificial intelligence models for daily rainfall prediction

(2020) Binh Thai Pham et al.   ATMOSPHERIC RESEARCH

Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

(2020) Viet-Ha Nhu et al.   Forests

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

(2020) Viet-Ha Nhu et al.   International Journal of Environmental Research and Public Health

Mapping and holistic design of natural hydraulic lime mortars

(2020) Maria Apostolopoulou et al.   CEMENT AND CONCRETE RESEARCH

A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

(2020) Shasha Lu et al.   Materials

Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models

(2020) Jian Zhou et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

(2019) Wei Chen et al.   Natural Resources Research

A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

(2019) Viet-Ha Nhu et al.   ENGINEERING WITH COMPUTERS

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

(2019) Lu Minh Le et al.   Materials

Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees

(2019) Ly et al.   Materials

Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction

(2019) Nguyen et al.   Applied Sciences-Basel

A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping

(2019) Dieu Tien Bui et al.   CATENA

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis

(2019) Binh Thai Pham et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam

(2019) Tran Van Phong et al.   Geocarto International

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

(2019) Pham et al.   Sustainability

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

(2019) Ly et al.   Applied Sciences-Basel

Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm

(2019) Yi Wang et al.   JOURNAL OF ENVIRONMENTAL MANAGEMENT

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

(2019) Hai-Bang Ly et al.   SENSORS

Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength

(2019) Hossein Moayedi et al.   Applied Sciences-Basel

Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

(2019) Hoang-Long Nguyen et al.   Applied Sciences-Basel

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

(2019) Moayedi et al.   Applied Sciences-Basel

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

(2019) Wei Chen et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

(2019) Hai-Bang Ly et al.   Applied Sciences-Basel

Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach

(2019) Chongchong Qi et al.   CHEMOSPHERE

Prediction of shear strength of soft soil using machine learning methods

(2018) Binh Thai Pham et al.   CATENA

A strength prediction model using artificial intelligence for recycling waste tailings as cemented paste backfill

(2018) Chongchong Qi et al.   JOURNAL OF CLEANER PRODUCTION

Spatial Prediction of Rainfall-Induced Landslides Using Aggregating One-Dependence Estimators Classifier

(2018) Binh Thai Pham et al.   Journal of the Indian Society of Remote Sensing

Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China

(2018) Wei Chen et al.   SCIENCE OF THE TOTAL ENVIRONMENT

A Comparison of Support Vector Machines and Bayesian Algorithms for Landslide Susceptibility Modeling

(2018) Binh Thai Pham et al.   Geocarto International

A Novel Hybrid Approach of Bayesian Logistic Regression and Its Ensembles for Landslide Susceptibility Assessment

(2018) Mousa Abedini et al.   Geocarto International

A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam)

(2018) Dieu Tien Bui et al.   ENGINEERING WITH COMPUTERS

Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

(2018) Mohsen Alizadeh et al.   Sustainability

A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil

(2018) Binh Thai Pham et al.   CATENA

Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine

(2017) Faming Huang et al.   ENGINEERING GEOLOGY

Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques

(2017) Wei Chen et al.   GEODERMA

Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

(2016) Maciej Zięba et al.   EXPERT SYSTEMS WITH APPLICATIONS

An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping

(2016) Brandon Heung et al.   GEODERMA

A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

(2016) Nikhil N. Vasu et al.   GEOMORPHOLOGY

A survey of machine learning for big data processing

(2016) Junfei Qiu et al.   EURASIP Journal on Advances in Signal Processing

Using Deep Learning for Image-Based Plant Disease Detection

(2016) Sharada P. Mohanty et al.   Frontiers in Plant Science

Monte Carlo simulations of mesoscale fracture of concrete with random aggregates and pores: a size effect study

(2015) Xiaofeng Wang et al.   CONSTRUCTION AND BUILDING MATERIALS

Development of an empirical correlation for predicting shear wave velocity of Christchurch soils from cone penetration test data

(2015) Christopher R. McGann et al.   SOIL DYNAMICS AND EARTHQUAKE ENGINEERING

Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods

(2015) Binh Thai Pham et al.   THEORETICAL AND APPLIED CLIMATOLOGY

Analytical Approach for Determination of Soil Shear Strength Parameters from CPT and CPTu Data

(2014) Hossein Motaghedi et al.   ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING

Machine Learning Techniques and Drug Design

(2012) J.C. Gertrudes et al.   CURRENT MEDICINAL CHEMISTRY

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

(2012) A. Besalatpour et al.   SOIL SCIENCE AND PLANT NUTRITION

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

(2011) Dieu Tien Bui et al.   COMPUTERS & GEOSCIENCES

Determination of the shear strength of unsaturated soils using the multistage direct shear test

(2011) Soonkie Nam et al.   ENGINEERING GEOLOGY

Extreme Learning Machine for Regression and Multiclass Classification

(2011) Guang-Bin Huang et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

Generalized stochastic approach for constitutive equation in linear elasticity: a random matrix model

(2011) J. Guilleminot et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Machine learning techniques applied to prediction of residual strength of clay

(2011) Sarat Das et al.   Open Geosciences

Application of suction stress for estimating unsaturated shear strength of soils using direct shear testing under low confining pressure

(2010) Byeong-Su Kim et al.   CANADIAN GEOTECHNICAL JOURNAL

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

(2010) M.A. Behrang et al.   SOLAR ENERGY

Use of SPT Blow Counts to Estimate Shear Strength Properties of Soils: Energy Balance Approach

(2009) Hiroshan Hettiarachchi et al.   JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING

A working guide to boosted regression trees

(2008) J. Elith et al.   JOURNAL OF ANIMAL ECOLOGY

Roughness and Unit Side Resistances of Drilled Shafts Socketed in Clay Shale and Limestone

(2008) Moon S. Nam et al.   JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING