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234 results on '"Nguyen, Huu Duy"'

31. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam.

Author

Nguyen, Huu Duy, Nguyen, Quoc Huy, Du, Quan Vu Viet, Pham, Viet Thanh, Pham, Le Tuan, Van Hoang, Thanh, Truong, Quang‐Hai, Bui, Quang‐Thanh, and Petrisor, Alexandru‐Ionut

Subjects
*LANDSLIDE hazard analysis, *LANDSLIDES, *ARTIFICIAL neural networks, *LANDSLIDE prediction, *DISTANCE education, *OPTIMIZATION algorithms, *REMOTE sensing, *MACHINE learning
Abstract

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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35. GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal).

Author

Ha, Minh-Cuong, Darrozes, José, Llubes, Muriel, Grippa, Manuela, Ramillien, Guillaume, Frappart, Frédéric, Baup, Frédéric, Tagesson, Håkan Torbern, Mougin, Eric, Guiro, Idrissa, Kergoat, Laurent, Nguyen, Huu Duy, Seoane, Lucia, Dufrechou, Gregory, and Vu, Phuong-Lan

Subjects
DROUGHT management, SOIL moisture, GLOBAL Positioning System, SOIL dynamics, DROUGHTS, CLIMATIC zones, ANTENNAS (Electronics), MOISTURE measurement
Abstract

With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals' waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Green synthesis of selenium nanorods using Muntigia calabura leaf extract: Effect of pH on characterization and bioactivities

Author

Nguyen Ngoc Kim Tuyen, Vo Khac Huy, Nguyen Huu Duy, Hoang An, Nguyen Thanh Hoai Nam, Nguyen Minh Dat, Quach Thi Thanh Huong, Nguyen Le Phuong Trang, Nguyen Do Phuong Anh, Lu Thi Mong Thy, Mai Thanh Phong, and Nguyen Huu Hieu

Abstract

In this study, selenium nanomaterials (nano-Se) were synthesized via a biological route, in which the leaf extract of Muntigia calabura was efficiently utilized as not only a reducing agent but also a surfactant to stabilize the produced nanostructures. In detail, the effect of pH level on the shape and bioactivities of nano-Se was thoroughly investigated, which revealed the formation of selenium nanorods at pH 8 with uniform sizes of 30–50 nm in diameter and several micrometers in length, corresponding to the Se-pH8 sample. Besides, the material also exhibited excellent bactericidal rates against both Gram-negative and Gram-positive strains, while the cytotoxicity test using the 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay elucidated the notably low toxicity of Se-pH8 towards the normal HEK293 cells. Therefore, the obtained results show the great potential of the nano-Se material in multiple industrial applications, particularly in the medical and pharmaceutical sectors.

Published
2023

39. GNSS-R monitoring of soil moisture dynamics in areas of severe drought:example of Dahra in the Sahelian climatic zone (Senegal)

Author

Ha, Minh-Cuong, Darrozes, José, Llubes, Muriel, Grippa, Manuela, Ramillien, Guillaume, Frappart, Frédéric, Baup, Frédéric, Tagesson, Håkan Torbern, Mougin, Eric, Guiro, Idrissa, Kergoat, Laurent, Nguyen, Huu Duy, Seoane, Lucia, Dufrechou, Gregory, Vu, Phuong-Lan, Ha, Minh-Cuong, Darrozes, José, Llubes, Muriel, Grippa, Manuela, Ramillien, Guillaume, Frappart, Frédéric, Baup, Frédéric, Tagesson, Håkan Torbern, Mougin, Eric, Guiro, Idrissa, Kergoat, Laurent, Nguyen, Huu Duy, Seoane, Lucia, Dufrechou, Gregory, and Vu, Phuong-Lan

Abstract

With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from similar to 3% to similar to 5% durinf the dry to the rainy season. Unfortunately, the GNSS signals' waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5-10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

Published
2023

42. GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

Author

Ha, Minh-Cuong, primary, Darrozes, José, additional, Llubes, Muriel, additional, Grippa, Manuela, additional, Ramillien, Guillaume, additional, Frappart, Frédéric, additional, Baup, Frédéric, additional, Tagesson, Håkan Torbern, additional, Mougin, Eric, additional, Guiro, Idrissa, additional, Kergoat, Laurent, additional, Nguyen, Huu Duy, additional, Seoane, Lucia, additional, Dufrechou, Gregory, additional, and Vu, Phuong-Lan, additional

Published
2022
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46. Application of GNSS Reflectometry in Water Level Monitoring using Low-cost GNSS Antenna: A Case Study in Tam Giang Lagoon, Thua Thien Hue Province

Author

Nguyen Huu Duy, Doan Quang Cuong, Pham Van Manh, Dinh Thi Bao Hoa, Nguyen Phuong Bac, Ha Minh Cuong, and Vu Phuong Lan

Subjects
General Medicine
Abstract

Extreme hydrological events become increasingly unpredictable due to climate change and sea-level rise, highlighting the importance of coastal sea level monitoring. This study aims to develop a Global Navigation Satellite System (GNSS) reflectometry technology that uses low-cost multi-frequency antennas to measure water levels. A multi-frequency GNSS antenna was installed in the Tam Giang lagoon area, Thua Thien Hue province, to collect data of GPS/GLONASS/Galileo/Beidou satellites at 1Hz from April 10 to April 29, 2022. Water level elevation is calculated from GNSS reflectometry data using Interference Pattern Technical (IPT) based on Signal-to-Noise Ratio (SNR). After filtering, the water level results are validated by data from the water level sensor located in the same location. The Root Mean Square Error between the water level from the GNSS - Reflectometry (GNSS– R) and the in situ measurement is 0,049 m and the correlation coefficient reaches 0,93 when combining different frequencies. The study results demonstrate that the multi-frequency GNSS-R station can be used as an additional method to measure water levels with an accuracy comparable to that of a standard tidal gauge. In addition, the study results also show the sensitivity of the GNSS reflected signal to weather conditions and the state of the sea surface, which is the basis for forecasting and early warning of storm surge extremes from GNSS reflectometry data.

Published
2022

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