VNU-UET Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T02:09:37ZEPrintshttp://eprints.uet.vnu.edu.vn/images/sitelogo.pnghttps://eprints.uet.vnu.edu.vn/eprints/2023-06-15T04:10:23Z2023-06-15T04:10:23Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4811This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/48112023-06-15T04:10:23ZPredicting land use effects on flood susceptibility using machine learning and remote sensing in coastal VietnamFlood damage is becoming increasingly severe in the context of climate change and changes in land use. Assessing the effects of these changes on floods is important, to help decision-makers and local authorities understand the causes of worsening floods and propose appropriate measures. The objective of this study was to evaluate the effects of climate and land use change on flood susceptibility in Thua Thien Hue province, Vietnam, using machine learning techniques (support vector machine (SVM) and random forest (RF)) and remote sensing. The machine learning models used a flood inventory including 1,864 flood locations and 11 conditional factors in 2017 and 2021, as the input data. The predictive capacity of the proposed models was assessed using the area under the curve (AUC), the root mean square error (RMSE), and the mean absolute error (MAE). Both proposed models were successful, with AUC values exceeding 0.95 in predicting the effects of climate and land use change on flood susceptibility. The RF model, with AUC = 0.98, outperformed the SVM model (AUC = 0.97). The areas most susceptible to flooding increased between 2017 and 2021 due to increased built-up area. The results of the study confirm machine learning's capacity to assess differences in flood susceptibility.Van Tich VuHuu Duy NguyenPhuong Lan VuMinh Cuong HaVan Dong BuiThi Oanh NguyenVan Hiep HoangThanh Kim Hue Nguyen2023-06-15T04:10:04Z2023-06-15T04:10:04Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4816This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/48162023-06-15T04:10:04ZFlood susceptibility mapping using advanced hybrid machine learning and CyGNSS: a case study of Nghe An province, VietnamFlooding is currently the most dangerous natural hazard. It can have heavy human and material impacts and, in recent years, flooding has tended to occur more frequently, due to changes our species has made to hydrological regimes, and due to climate change. It is of the utmost importance that new models are developed to predict and map flood susceptibility with high accuracy, to support decision-makers and planners in designing more effective flood management strategies. The objective of this study is the development of a new method based on state-of-the-art machine learning and remote sensing, namely random forest (RF), dingo optimization algorithm, a weighted chimp optimization algorithm (WChOA), and particle swarm optimization to build flood susceptibility maps in the Nghe An province of Vietnam. The CyGNSS system was used to collect soil moisture data to integrate into the susceptibility model. A total of 1650 flood locations and 14 conditioning factors were used to construct the model. These data were divided at a ratio of 60/20/20 to train, validate, and test the model, respectively. In addition, various statistical indices, namely root-mean-square error, receiver operation characteristic, mean absolute error, and the coefficient of determination (R2), were used to assess the performance of the model. The results for all the models were good, with an AUC value of + 0.9. The RF-WChOA model performed best, with an AUC value of 0.99. The proposed models can predict and map flood susceptibility with high accuracy.Huu Duy NguyenPhương Lan VuMinh Cuong HaThi Bao Hoa DinhThuy Hang NguyenTich Phuc HoangQuang Cuong DoanVan Manh PhamDinh Kha Dang2022-08-22T03:57:57Z2022-08-22T03:57:57Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4750This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/47502022-08-22T03:57:57ZMachine Learning and Remote Sensing Application for Extreme Climate Evaluation: Example of Flood Susceptibility in the Hue Province, Central Vietnam RegionFloods are the most frequent natural hazard globally and incidences have been increasing in recent years as a result of human activity and global warming, making significant impacts on people’s livelihoods and wider socio-economic activities. In terms of the management of the environment and water resources, precise identification is required of areas susceptible to flooding to support planners in implementing effective prevention strategies. The objective of this study is to develop a novel hybrid approach based on Bald Eagle Search (BES), Support Vector Machine (SVM), Random Forest (RF), Bagging (BA) and Multi-Layer Perceptron (MLP) to generate a flood susceptibility map in Thua Thien Hue province, Vietnam. In total, 1621 flood points and 14 predictor variables were used in this study. These data were divided into 60 for model training, 20 for model validation and 20 for testing. In addition, various statistical indices were used to evaluate the performance of the model, such as Root Mean Square Error (RMSE), Receiver Operation Characteristics (ROC), and Mean Absolute Error (MAE). The results show that BES, for the first time, successfully improved the performance of individual models in building a flood susceptibility map in Thua Thien Hue, Vietnam, namely SVM, RF, BA and MLP, with high accuracy (AUC > 0.9). Among the models proposed, BA-BES was most effective with AUC = 0.998, followed by RF-BES (AUC = 0.998), MLP-BES (AUC = 0.998), and SVM-BES (AUC = 0.99). The findings of this research can support the decisions of local and regional authorities in Vietnam and other countries regarding the construction of appropriate strategies to reduce damage to property and human life, particularly in the context of climate change.Minh Cuong HaPhuong Lan VuHuu Duy NguyenTich Phuc HoangDinh Duc DangThi Bao Hoa DinhGheorghe ŞerbanIoan RusPetre Brețcan2021-06-28T00:10:30Z2021-06-28T00:10:30Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4101This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/41012021-06-28T00:10:30ZResearch and application of D-LINKNET network to solve the problem of detecting road from WorldView-3 satellite image in Cau Giay district, HanoiIn this paper, we have outlined the need for solving the road detection problem, from which to compare and find the type of data to be used as Worldview-3 satellite images, to find out the method of success. Very good achievement in the DeepGlobe contest is D-LinkNet. This thesis has collected Worldview-3 and yandex image data for preprocessing data. We have collected and pre-processed Worldview-3 data, deployed and installed D-LinkNet. Application of evaluation method of IoU point of DeepGlobe. In addition, learn how to develop a realistic assessment method rather than a long-distance evaluation. D-LinkNet works well in sparsely populated areas, big roads such as suburbs or new urban areas, resettlement areas. In densely populated urban areas, small alleys are obscured by houses and trees, making it difficult and difficult to detect roads. Some areas with the same spectrum as the road are also mislabeled.Quang Dao Ledaolq@fimo.edu.vnNhat Nam Nguyennamnn@fimo.edu.vnQuang Thang Luuthanglq@fimo.edu.vnDuc Van Havanhd@fimo.edu.vnMinh Cuong Hacuonghm@vnu.edu.vnBao Son Phamsonpb@vnu.edu.vnQuang Hung Buihungbq@fimo.edu.vn2021-06-28T00:08:12Z2021-06-28T00:08:12Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/4100This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/41002021-06-28T00:08:12ZApplication of GNSS-Reflectometry to evaluate the ability to detect moisture changes of sandy soilsWith population growth, water demand is expected to tremendously increase in the next decades. The optimization of water allocation for agriculture requires soil moisture monitoring. Recent studies suggested to take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations, to retrieve soil moisture variations. This active remote sensing technique, known as GNSS Reflectometry (GNSS-R), consists in comparing the interference of reflected waves by the ground and those which come directly from satellites. It offers a wide range of applications in Earth sciences and particularly in soil moisture monitoring, this technique has shown their efficiency for soil with high clay content. It namely presents the advantage of sensing a whole surface around a reference GNSS antenna. In this article, we focus on soil moisture monitoring of sandy areas. The study site is a beach volley field, located in the Paul Sabatier University campus in Toulouse, France, the soil contains 100% of sand. We demonstrated that the waves of the GNSS signals penetrate deeply into the soil and reduce the interest of GNSS-R surface moisture measurements. However, it is possible to retrieve a correct estimate of the soil moisture at 0.1m depth and to obtain a very good temporal monitoring with the benefit of a spatial resolution directly correlated to the antenna height.Minh Cuong Hacuonghm@vnu.edu.vnPhuong Lan Vuvuphuonglan@hus.edu.vn2020-01-08T01:07:43Z2020-01-08T01:08:23Zhttp://eprints.uet.vnu.edu.vn/eprints/id/eprint/3692This item is in the repository with the URL: http://eprints.uet.vnu.edu.vn/eprints/id/eprint/36922020-01-08T01:07:43ZỨng dụng công nghệ GNSS-R (Phản xạ GNSS) để phát hiện các sự kiện thuỷ văn cực đoan (ví dụ cơn bão Xynthia năm 2010 tại Pháp)In this study, 3 months of records (January-March 2010) acquired by a geodetic GNSS station from the permanent network of RGP (Réseau GNSS Permanent), located in SCOA station, in the south of the Bay of Biscay to identify the Xynthia storm (hit the French Atlantic coast on February 28, 2010). This storm causing large floods and damages for the Gironde estuary. The separation of the tide components and the identification of Xynthia storm was achieved using the Interference Pattern Technique (IPT), a singular spectrum analysis (SSA) and a continuous wavelet transform (CWT).Phuong Lan Vuvuphuonglan@hus.edu.vnMinh Cuong Hacuonghm@vnu.edu.vnThi Bao Hoa Ddinhdinhthibaohoa@vnu.edu.vnThi Thuy Hang Nguyennguyenthuyhang@vnu.edu.vnQuang Thanh Buibuiquangthanh@vnu.edu.vnVan Manh Phampvmanh84@vnu.edu.vnVu Dong Phampvd2741996@gmail.com