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Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. A new approach based on biology-inspired metaheuristic algorithms in combination with random forest to enhance the flood susceptibility mapping. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 345:118790. [PMID: 37647734 DOI: 10.1016/j.jenvman.2023.118790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 07/17/2023] [Accepted: 08/09/2023] [Indexed: 09/01/2023]
Abstract
Flash floods are one of the worst natural disasters, causing massive economic losses and many deaths. Creating a flood susceptibility map (FSM) that pinpoints the areas most at risk of flooding is a crucial non-structural solution for managing floods. This study aimed to assess the efficacy of combinations of the random forest (RF) model with three biology-inspired metaheuristic algorithms, namely invasive weed optimization (IWO), slime mould algorithm (SMA), and satin bowerbird optimization (SBO), for flood susceptibility mapping in Estahban town, Iran. Initially, synthetic-aperture radar (SAR) (Sentinel-1) and optical (Landsat-8) satellite images were integrated to monitor the flooded areas during the July 2022 monsoon in the study area. A dataset of 509 flood occurrence points was created to identify flood-prone areas using remote sensing techniques, considering the monitored flood areas. The dataset also included twelve flood-related criteria: topography, land cover, and climate. The holdout method was employed for modeling, with a ratio of 70:30 used for the train/test split. Data pre-processing techniques were conducted to improve model performance, including determining criteria importance and addressing multicollinearity issues using certainty factor (CF), multicollinearity, and information gain ratio (IGR) methods. Then FSM was prepared using RF, RF-IWO, RF-SBO, and RF-SMA models. The findings of this research revealed that the RF-IWO model was the best predictive model of flood susceptibility modeling, with root-mean-square-error (RMSE) (0.211 and 0.0.27), mean-absolute-error (MAE) (0.103 and 0.15), and coefficient-of-determination (R2) (0.821 and 0.707) in the training and testing phases, respectively. Receiver operating characteristic (ROC) curve analysis of FSM revealed that the most accurate models were the RF-IWO (area under the curve (AUC) = 0.983), RF-SBO (AUC = 0.979), RF-SMA (AUC = 0.963), and RF (AUC = 0.959), respectively. Integrating biology-inspired computing algorithms with machine learning algorithms presents a novel approach to enhancing the accuracy of FSMs.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
| | - Abolghasem Sadeghi-Niaraki
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
| | - Soo-Mi Choi
- Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.
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Sun C, Tian H, Mazurczyk W, Chang C, Cai Y, Chen Y. Towards blind detection of steganography in low‐bit‐rate speech streams. INT J INTELL SYST 2022. [DOI: 10.1002/int.23077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Congcong Sun
- College of Computer Science and Technology National Huaqiao University Xiamen China
- Xiamen Key Laboratory of Data Security and Blockchain Technology National Huaqiao University Xiamen China
- Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University Xiamen China
| | - Hui Tian
- College of Computer Science and Technology National Huaqiao University Xiamen China
- Xiamen Key Laboratory of Data Security and Blockchain Technology National Huaqiao University Xiamen China
- Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University Xiamen China
| | - Wojciech Mazurczyk
- Institute of Computer Science Warsaw University of Technology Warsaw Poland
| | - Chin‐Chen Chang
- Department of Information and Computer Science Feng Chia University Taichung Taiwan
| | - Yiqiao Cai
- College of Computer Science and Technology National Huaqiao University Xiamen China
- Xiamen Key Laboratory of Data Security and Blockchain Technology National Huaqiao University Xiamen China
- Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University Xiamen China
| | - Yonghong Chen
- College of Computer Science and Technology National Huaqiao University Xiamen China
- Xiamen Key Laboratory of Data Security and Blockchain Technology National Huaqiao University Xiamen China
- Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University Xiamen China
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Joo C, Park H, Kwon H, Lim J, Shin E, Cho H, Kim J. Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data. Polymers (Basel) 2022; 14:polym14173500. [PMID: 36080575 PMCID: PMC9459971 DOI: 10.3390/polym14173500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/11/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
Manufacturing polypropylene (PP) composites to meet customers’ needs is difficult, time-consuming, and costly, owing to the ever-increasing diversity and complexity of the corresponding specifications and the trial-and-error method currently used to satisfy the required physical properties. To address this issue, we developed three models for predicting the physical properties of PP composites using three machine learning (ML) methods: multiple linear regression (MLR), deep neural network (DNN), and random forest (RF). Further, the industrial data of 811 recipes were acquired to verify the developed models. Data categorization was performed to account for the differences between data and the fact that different recipes require different materials. The three models were then deployed to predict the flexural strength (FS), melting index (MI), and tensile strength (TS) of the PP composites in nine case studies. The predictive performance results differed according to the physical properties of the composites. The FS and MI prediction models with MLR exhibited the highest R2 values of 0.9291 and 0.9406. The TS model with DNN exhibited the highest R2 value of 0.9587. The proposed models and study findings are useful for predicting the physical properties of PP composites for recipes and the development of new recipes with specific physical properties.
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Affiliation(s)
- Chonghyo Joo
- Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, 55, Jongga-ro, Jung-gu, Ulsan 44413, Korea
- Department of Chemical and Biomolecular Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Hyundo Park
- Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, 55, Jongga-ro, Jung-gu, Ulsan 44413, Korea
- Department of Chemical and Biomolecular Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Hyukwon Kwon
- Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, 55, Jongga-ro, Jung-gu, Ulsan 44413, Korea
- Department of Chemical and Biomolecular Engineering, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
| | - Jongkoo Lim
- Research & Development Center, GS Caltex Corporation, 359, Expo-ro, Yuseon-gu, Daejeon 34122, Korea
| | - Eunchul Shin
- Research & Development Center, GS Caltex Corporation, 359, Expo-ro, Yuseon-gu, Daejeon 34122, Korea
| | - Hyungtae Cho
- Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, 55, Jongga-ro, Jung-gu, Ulsan 44413, Korea
| | - Junghwan Kim
- Green Materials and Processes R&D Group, Korea Institute of Industrial Technology, 55, Jongga-ro, Jung-gu, Ulsan 44413, Korea
- Correspondence:
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Berladir K, Zhyhylii D, Gaponova O, Krmela J, Krmelová V, Artyukhov A. Modeling of Polymer Composite Materials Chaotically Reinforced with Spherical and Cylindrical Inclusions. Polymers (Basel) 2022; 14:2087. [PMID: 35631969 PMCID: PMC9144413 DOI: 10.3390/polym14102087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/14/2022] [Accepted: 05/18/2022] [Indexed: 11/16/2022] Open
Abstract
The technical and economic efficiency of new PCMs depends on the ability to predict their performance. The problem of predicting the properties of PCMs can be solved by computer simulation by the finite element method. In this work, an experimental determination of the physical and mechanical properties of PTFE PCMs depending on the concentration of fibrous and dispersed filler was carried out. A finite element model in ANSYS APDL was built to simulate the strength and load-bearing capacity of the material with the analysis of damage accumulation. Verification of the developed computer model to predict the mechanical properties of composite materials was performed by comparing the results obtained during field and model experiments. It was found that the finite element model predicts the strength of chaotically reinforced spherical inclusions of composite materials. This is due to the smoothness of the filler surfaces and the lack of filler dissection in the model. Instead, the prediction of the strength of a finite element model of chaotically reinforced cylindrical inclusions of composite materials requires additional analysis. The matrix and the fibrous filler obviously have stress concentrators and are both subject to the difficulties of creating a reliable structural model.
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Affiliation(s)
- Kristina Berladir
- Department of Applied Materials Science and Technology of Constructional Materials, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
| | - Dmytro Zhyhylii
- Department of Computational Mechanics Named after Volodymyr Martsynkovskyy, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
| | - Oksana Gaponova
- Department of Applied Materials Science and Technology of Constructional Materials, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
| | - Jan Krmela
- Faculty of Mechanical Engineering, J. E. Purkyně University in Ustí nad Labem, Pasteurova 1, 400 96 Ustí nad Labem, Czech Republic;
| | - Vladimíra Krmelová
- Faculty of Industrial Technologies in Púchov, Alexander Dubček University of Trenčín, I. Krasku 491/30, 02001 Púchov, Slovakia;
| | - Artem Artyukhov
- Academic and Research Institute of Business, Economics and Management, Sumy State University, 2, Rymskogo-Korsakova St., 40007 Sumy, Ukraine;
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