Hossein Moayedi

Bio: Hossein Moayedi is an academic researcher from Ton Duc Thang University. The author has contributed to research in topics: Metaheuristic & Artificial neural network. The author has an hindex of 38, co-authored 246 publications receiving 4909 citations. Previous affiliations of Hossein Moayedi include University of Gilan & Universiti Teknologi Malaysia.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Abstract: In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.

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

Abstract: With the increasing threat of recurring landslides, susceptibility maps are expected to play a bigger role in promoting our understanding of future landslides and their magnitude. This study describes the development and validation of a spatially explicit deep learning (DL) neural network model for the prediction of landslide susceptibility. A geospatial database was generated based on 217 landslide events from the Muong Lay district (Vietnam), for which a suite of nine landslide conditioning factors was derived. The Relief-F feature selection method was employed to quantify the utility of the conditioning factors for developing the landslide predictive model. Several performance metrics demonstrated that the DL model performed well both in terms of the goodness-of-fit with the training dataset (AUC = 0.90; accuracy = 82%; RMSE = 0.36) and the ability to predict future landslides (AUC = 0.89; accuracy = 82%; RMSE = 0.38). The efficiency of the model was compared to the quadratic discriminant analysis, Fisher's linear discriminant analysis, and multi-layer perceptron neural network. A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over these other models. The insights provided from this study will be valuable for further development of landslide predictive models and spatially explicit assessment of landslide-prone regions around the world.

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Abstract: Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.

American Society for Testing and Materials

Abstract: The American Society for Testing and Materials (ASTM) is an independent organization devoted to the development of standards.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Surface and Colloid Science

Abstract: 1: Magnetic Particles: Preparation, Properties and Applications: M. Ozaki. 2: Maghemite (gamma-Fe2O3): A Versatile Magnetic Colloidal Material C.J. Serna, M.P. Morales. 3: Dynamics of Adsorption and Oxidation of Organic Molecules on Illuminated Titanium Dioxide Particles Immersed in Water M.A. Blesa, R.J. Candal, S.A. Bilmes. 4: Colloidal Aggregation in Two-Dimensions A. Moncho-Jorda, F. Martinez-Lopez, M.A. Cabrerizo-Vilchez, R. Hidalgo Alvarez, M. Quesada-PMerez. 5: Kinetics of Particle and Protein Adsorption Z. Adamczyk.