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Tong Q, Wu J, Zhu Z, Zhang M, Xing H. STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea-Land segmentation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 357:120773. [PMID: 38555845 DOI: 10.1016/j.jenvman.2024.120773] [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: 02/01/2024] [Revised: 03/22/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
Extraction of coastline from optical remote sensing images is of paramount importance for coastal zone management, erosion monitoring, and intelligent ocean construction. However, nearshore marine environment complexity presents a challenge when capturing small-scale and detailed information regarding coastlines. Furthermore, the presence of numerous tidal flats, suspended sediments, and coastal biological communities exacerbates the reduction in segmentation accuracy, which is particularly noticeable in medium-high-resolution remote sensing image segmentation tasks. Most previous related studies, based primarily on convolutional neural networks (CNNs) or traditional feature extraction methods, faced challenges in detailed pixel-level refinement and lacked comprehensive understanding of the studied images. Therefore, we proposed a new U-shaped deep learning model (STIRUnet) that combines the excellent global modeling ability of SwinTransformer with an improved CNN using an inverted residual module. The proposed method has the capability of global supervised feature learning and layer-by-layer feature extraction, and we conducted sea-land segmentation experiments using GF-HNCD and BSD remote sensing image datasets to validate the performance of the proposed model. The results indicate the following: 1) suspended sediments and coastal biological communities are major contributors to coastline blurring, and 2) the recovery of minute features (e.g., narrow watercourses and microscale artificial structures) effectively enhances edge details and leads to more realistic segmentation outcomes. The findings of this study are highly important in relation of accurate extraction of sea-land information in complex marine environments, and they offer novel insights regarding mixed-pixel identification.
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Affiliation(s)
- Qixiang Tong
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Jiawei Wu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Zhipeng Zhu
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Min Zhang
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China
| | - Haihua Xing
- College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
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Pham TT, Dang KB, Giang TL, Hoang THN, Le VH, Ha HN. Deep learning models for monitoring landscape changes in a UNESCO Global Geopark. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120497. [PMID: 38417365 DOI: 10.1016/j.jenvman.2024.120497] [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: 08/29/2023] [Revised: 01/13/2024] [Accepted: 02/22/2024] [Indexed: 03/01/2024]
Abstract
By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development.
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Affiliation(s)
- Thi Tram Pham
- Institute of Human Geography, Vietnam Academy of Social Sciences, No.176, Thai Ha, Dong Da, Hanoi, Viet Nam.
| | - Kinh Bac Dang
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam.
| | - Tuan Linh Giang
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam; VNU Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, 336 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam.
| | - Thi Huyen Ngoc Hoang
- Institute of Geography, Vietnam Academy of Science and Technology, 18, Hoang Quoc Viet, Cau Giay, Hanoi, Viet Nam.
| | - Van Ha Le
- Institute of Human Geography, Vietnam Academy of Social Sciences, No.176, Thai Ha, Dong Da, Hanoi, Viet Nam.
| | - Huy Ngoc Ha
- Vietnam Institute of Economics, Vietnam Academy of Social Sciences, No.1, Lieu Giai, Ba Dinh, Hanoi, Viet Nam.
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Dang KB, Nguyen CQ, Tran QC, Nguyen H, Nguyen TT, Nguyen DA, Tran TH, Bui PT, Giang TL, Nguyen DA, Lenh TA, Ngo VL, Yasir M, Nguyen TT, Ngo HH. Comparison between U-shaped structural deep learning models to detect landslide traces. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169113. [PMID: 38065499 DOI: 10.1016/j.scitotenv.2023.169113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/02/2023] [Accepted: 12/03/2023] [Indexed: 12/24/2023]
Abstract
Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.
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Affiliation(s)
- Kinh Bac Dang
- Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam
| | - Cong Quan Nguyen
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam.
| | - Quoc Cuong Tran
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Hieu Nguyen
- Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam
| | - Trung Thanh Nguyen
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Duc Anh Nguyen
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Trung Hieu Tran
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Phuong Thao Bui
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Tuan Linh Giang
- Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam; VNU Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, 336 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam
| | - Duc Anh Nguyen
- Quaternary - Geomorphology Association, Vietnam Academy of Science and Technology, 84, Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Tu Anh Lenh
- Institute of Geological Sciences, Vietnam Academy of Science and Technology, 84 Chua Lang, Dong Da, Hanoi, Viet Nam
| | - Van Liem Ngo
- Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam
| | - Muhammad Yasir
- College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
| | - Thu Thuy Nguyen
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Huu Hao Ngo
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia.
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Sirimewan D, Bazli M, Raman S, Mohandes SR, Kineber AF, Arashpour M. Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119908. [PMID: 38169254 DOI: 10.1016/j.jenvman.2023.119908] [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: 09/02/2023] [Revised: 12/04/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024]
Abstract
The construction industry generates a substantial volume of solid waste, often destinated for landfills, causing significant environmental pollution. Waste recycling is decisive in managing waste yet challenging due to labor-intensive sorting processes and the diverse forms of waste. Deep learning (DL) models have made remarkable strides in automating domestic waste recognition and sorting. However, the application of DL models to recognize the waste derived from construction, renovation, and demolition (CRD) activities remains limited due to the context-specific studies conducted in previous research. This paper aims to realistically capture the complexity of waste streams in the CRD context. The study encompasses collecting and annotating CRD waste images in real-world, uncontrolled environments. It then evaluates the performance of state-of-the-art DL models for automatically recognizing CRD waste in-the-wild. Several pre-trained networks are utilized to perform effectual feature extraction and transfer learning during DL model training. The results demonstrated that DL models, whether integrated with larger or lightweight backbone networks can recognize the composition of CRD waste streams in-the-wild which is useful for automated waste sorting. The outcome of the study emphasized the applicability of DL models in recognizing and sorting solid waste across various industrial domains, thereby contributing to resource recovery and encouraging environmental management efforts.
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Affiliation(s)
- Diani Sirimewan
- Department of Civil Engineering, Monash University, Melbourne, Australia.
| | - Milad Bazli
- Faculty of Science and Technology, Charles Darwin University, Australia.
| | - Sudharshan Raman
- Civil Engineering Discipline, School of Engineering, Monash University, Malaysia.
| | | | - Ahmed Farouk Kineber
- Department of Civil Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia.
| | - Mehrdad Arashpour
- Department of Civil Engineering, Monash University, Melbourne, Australia.
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Giang TL, Bui QT, Nguyen TDL, Dang VB, Truong QH, Phan TT, Nguyen H, Ngo VL, Tran VT, Yasir M, Dang KB. Coastal landscape classification using convolutional neural network and remote sensing data in Vietnam. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 335:117537. [PMID: 36842358 DOI: 10.1016/j.jenvman.2023.117537] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
The length of global coastline is about 356 thousand kilometers with various dynamic natural and anthropogenic. Although the number of studies on coastal landscape categorization has been increasing, it is still difficult to distinguish precisely them because the used methods commonly are traditional qualitative ones. With the leverage of remote sensing data and GIS tools, it helps categorize and identify a variety of features on land and water based on multi-source data. The aim of study is using different natural - social profile data obtained from ALOS, NOAA, and multi-temporal Landsat satellite images as input data of the convolutional-neural-network (CvNet) models for coastal landscape classification. Studies used 900 cut-line samples which represent coastal landscapes in Vietnam for training and optimizing CvNet models. As a result, nine coastal landscapes were identified including: deltas, alluvial, mature and young sand dunes, cliff, lagoon, tectonic, karst, and transitional landscapes. Three CvNet models using three different optimizer types classified the landscapes of other 1150 cut-lines in Vietnam with the accuracies about 98% and low loss function value. Excepting dalmatian, karst and delta coastal landscapes, five others distribute heterogeneous along the coasts in Vietnam. Therefore, the evaluation of additional natural components is necessary and CvNet model have ability to update new landscape types in variety of tropical nation as a step toward coastal landscape classification at both national and global scales.
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Affiliation(s)
- Tuan Linh Giang
- VNU Institute of Vietnamese Studies and Development Sciences, Vietnam National University, Hanoi, 336 Nguyen Trai, 10000, Hanoi, Viet Nam; VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Quang Thanh Bui
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Thi Dieu Linh Nguyen
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Van Bao Dang
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Quang Hai Truong
- VNU Institute of Vietnamese Studies and Development Sciences, Vietnam National University, Hanoi, 336 Nguyen Trai, 10000, Hanoi, Viet Nam.
| | - Trong Trinh Phan
- Institute of Geological Sciences, Vietnam Academy of Science and Technology (VAST), Dong Da, 10000, Hanoi, Viet Nam.
| | - Hieu Nguyen
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Van Liem Ngo
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Van Truong Tran
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
| | - Muhammad Yasir
- College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China.
| | - Kinh Bac Dang
- VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, 10000, Hanoi, Viet Nam.
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Arashpour M. AI explainability framework for environmental management research. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 342:118149. [PMID: 37187074 DOI: 10.1016/j.jenvman.2023.118149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/08/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Deep learning networks powered by AI are essential predictive tools relying on image data availability and processing hardware advancements. However, little attention has been paid to explainable AI (XAI) in application fields, including environmental management. This study develops an explainability framework with a triadic structure to focus on input, AI model and output. The framework provides three main contributions. (1) A context-based augmentation of input data to maximize generalizability and minimize overfitting. (2) A direct monitoring of AI model layers and parameters to use leaner (lighter) networks suitable for edge device deployment, (3) An output explanation procedure focusing on interpretability and robustness of predictive decisions by AI networks. These contributions significantly advance state of the art in XAI for environmental management research, offering implications for improved understanding and utilization of AI networks in this field.
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Affiliation(s)
- Mehrdad Arashpour
- Department of Civil Engineering, Monash University, Melbourne, VIC, 3800, Australia.
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Andria G, Scarpetta M, Spadavecchia M, Affuso P, Giaquinto N. SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094491. [PMID: 37177695 PMCID: PMC10181759 DOI: 10.3390/s23094491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.
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Affiliation(s)
- Gregorio Andria
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
| | - Marco Scarpetta
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
| | - Maurizio Spadavecchia
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
| | - Paolo Affuso
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
| | - Nicola Giaquinto
- Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, Italy
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Sun L, Zhu J, Tan J, Li X, Li R, Deng H, Zhang X, Liu B, Zhu X. Deep learning-assisted automated sewage pipe defect detection for urban water environment management. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 882:163562. [PMID: 37084915 DOI: 10.1016/j.scitotenv.2023.163562] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/13/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023]
Abstract
A healthy sewage pipe system plays a significant role in urban water management by collecting and transporting wastewater and stormwater, which can be assessed by hydraulic model. However, sewage pipe defects have been observed frequently in recent years during regular pipe maintenance according to the captured interior videos of underground pipes by closed-circuit television (CCTV) robots. In this case, hydraulic model constructed based on a healthy pipe would produce large deviations with that in real hydraulic performance and even be out of work, which can result in unanticipated damages such as blockage collapse or stormwater overflows. Quick defect evaluation and defect quantification are the precondition to achieve risk assessment and model calibration of urban water management, but currently pipe defects assessment still largely relies on technicians to check the CCTV videos/images. An automated sewage pipe defect detection system is necessary to timely determine pipe issues and then rehabilitate or renew sewage pipes, while the rapid development of deep learning especially in recent five years provides a fantastic opportunity to construct automated pipe defect detection system by image recognition. Given the initial success of deep learning application in CCTV interpretation, the review (i) integrated the methodological framework of automated sewage pipe defect detection, including data acquisition, image pre-processing, feature extraction, model construction and evaluation metrics, (ii) discussed the state-of-the-art performance of deep learning in pipe defects classification, location, and severity rating evaluation (e.g., up to ~96 % of accuracy and 140 FPS of processing speed), and (iii) proposed risk assessment and model calibration in urban water management by considering pipe defects. This review introduces a novel practical application-oriented methodology including defect data acquisition by CCTV, model construction by deep learning, and model application, provides references for further improving accuracy and generalization ability of urban water management models in practical application.
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Affiliation(s)
- Lianpeng Sun
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Jinjun Zhu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Jinxin Tan
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xianfeng Li
- School of Computer Science and Engineering, Macau University of Science and Technology, Macau
| | - Ruohong Li
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China
| | - Huanzhong Deng
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xinyang Zhang
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Bingyou Liu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
| | - Xinzhe Zhu
- School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China; Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-sen University, Guangzhou 510275, China.
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