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Novel Time Series Bagging Based Hybrid Models for Predicting Historical Water Levels in the Mekong Delta Region, Vietnam

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1431-1449页

作者： nguyen thanh hoan nguyen Van Dung Ho Le Thu Hoa Thuy Quynh Nadhir Al-Ansari Tran Van Phong Phan Trong Trinh Dam Duc nguyen Hiep Van Le Hanh Bich Thi nguyen Mahdis Amiri Indra Prakash Binh Thai Pham Institute of Geography Vietnam Academy of Science and TechnologyHanoi10000Viet Nam Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Institute of Geological Sciences Vietnam Academy of Science and Technology(VAST)Dong DaHanoi10000Viet Nam University of Transport Technology Thanh XuanHa Noi10000Viet Nam Department of Watershed&Arid Zone Management Gorgan University of Agricultural Sciences&Natural ResourcesGorgan***Iran DDG(R)Geological Survey of India Gandhinagar382010India

Water level predictions in the river,lake and delta play an important role in flood *** year Mekong River delta of Vietnam is experiencing flood due to heavy monsoon rains and high *** subsidence may also aggravate flooding problems in this ***,accurate predictions of water levels in this region are very important to forewarn the people and authorities for taking timely adequate remedial measures to prevent losses of life and *** are so many methods available to predict the water levels based on historical data but nowadays Machine Learning(ML)methods are considered the best tool for accurate *** this study,we have used surface water level data of 18 water level measurement stations of the Mekong River delta from 2000 to 2018 to build novel time-series Bagging based hybrid ML models namely:Bagging(RF),Bagging(SOM)and Bagging(M5P)to predict historical water levels in the study *** of the Bagging-based hybrid models were compared with Reduced Error Pruning Trees(REPT),which is a benchmark ML *** data of 19 years period was divided into 70:30 ratio for the *** data of the period 1/2000 to 5/2013(which is about 70%of total data)was used for the training and for the period 5/2013 to 12/2018(which is about 30%of total data)was used for testing(validating)the *** of the models was evaluated using standard statistical measures:Coefficient of Determination(R2),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).Results show that the performance of all the developed models is good(R2>0.9)for the prediction of water levels in the study ***,the Bagging-based hybrid models are slightly better than another model such as ***,these Bagging-based hybrid time series models can be used for predicting water levels at Mekong data.

关键词： Computational techniques bagging water level time series algorithms

Forest Change and Its Effect on Biomass in Yok Don National Park in Central Highlands of Vietnam Using Ground Data and Geospatial Techniques

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Advances in Remote Sensing 2015年第2期4卷 108-118页

作者： nguyen Viet Luong Ryutaro Tateishi nguyen thanh hoan To Trong Tu Space Technology Institute Vietnam Academy of Science and Technology Hanoi Vietnam Center for Environmental Remote Sensing Chiba University Chiba Japan Institute of Geography Vietnam Academy of Science and Technology Hanoi Vietnam

This paper assesses the changes in forest cover in Yok Don National Park of Vietnam between 2004 and 2010, and the implications of such changes on the biomass stocks of this national park. Remote sensing and GIS tools along with the ground truth data collected from the field were employed for classifying the forest types of the study area from SPOT HRV satellite imagery for years 2004 and 2010. The total area considered in this study is 115.5 thousand ha. Five different categories of forests were identified. The results demonstrated that between 2004 and 2010, the Evergreen broad leaved rich quality forest decreased by 11.2 thousand ha (3.5 Mega tons of biomass) and the Dry open dipterocarps medium quality forest decreased by 15.3 thousand ha (2.5 Mega tons of biomass). In that time period, the Evergreen broad leaved medium quality forest increased by 3.2 thousand ha (0.8 Mega tons of biomass), the Evergreen broad leaved poor quality forest increased by 2.5 thousand ha (0.24 Mega tons of biomass), and the Dry open dipterocarps poor quality forest increased by 3.2 thousand ha (0.69 Mega tons of biomass). Total biomass of the study area decreased by 4.3 Mega tons.

关键词： Satellite Data SPOT HRV Land Cover Change Tropical Forest Biomass

Production of global land cover data-GLCNMO

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International Journal of Digital Earth 2011年第1期4卷 22-49页

作者： Ryutaro Tateishi Bayaer Uriyangqai Hussam Al-Bilbisi Mohamed Aboel Ghar Javzandulam Tsend-Ayush Toshiyuki Kobayashi Alimujiang Kasimu nguyen thanh hoan Adel Shalaby Bayan Alsaaideh Tsevenge Enkhzaya Gegentana Hiroshi P.Sato Center for Environmental Remote Sensing(CEReS) Chiba UniversityChibaJapan College of Geographical Science Inner Mongolia Normal UniversityHuhhotChina Geography Department University of JordanAmmanJordan National Authority for Remote Sensing and Space Sciences(NARSS) CairoEgypt School of Computer and Information Technology Mongolian State University of EducationUlaanbaatarMongolia Institute of Geographical Science and Tourism Xinjiang Normal UniversityUrumqiChina Geographical Survey Institute TsukubaJapan

Global land cover is one of the fundamental contents of Digital *** Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover datasetGlobal Land Cover by National Mapping *** has 20 land cover classes defined using the Land Cover Classification *** them,14 classes were derived using supervised *** remaining six were classified independently:urban,tree open,mangrove,wetland,snow/ice,*** source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of *** data for supervised classification were collected using Landsat images,MODIS NDVI seasonal change patterns,Google Earth,Virtual Earth,existing regional maps,and expert’s *** overall accuracy is 76.5%and the overall accuracy with the weight of the mapped area coverage is 81.2%.The data are available from the Global Mapping project website(http://***/).TheMODISdata used,land cover training data,and a list of existing regional maps are also available from the CEReS *** mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.

关键词： land cover remote sensing Digital Earth training data

Effectiveness of Sentinel-1-2 Multi-Temporal Composite Images for Land-Cover Monitoring in the Indochinese Peninsula

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Journal of Geoscience and Environment Protection 2020年第9期8卷 24-32页

作者： nguyen thanh hoan Ram C. Sharma nguyen Van Dung Dang Xuan Tung Institute of Geography Vietnam Academy of Science and Technology Hanoi Vietnam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Vietnam Department of Informatics Tokyo University of Information Sciences Chiba Japan Institute of Geological Sciences Vietnam Academy of Science and Technology Hanoi Vietnam

The Indochinese Peninsula, which contains two thirds of the world’s tropical forests, however, is one of the world’s most threatened habitat with some of the highest rates of deforestation and land use changes. Availability of higher resolution satellite data collected by the likes of Landsat 8 and Sentinel-1-2 has brought new opportunities for precise land cover monitoring in recent years. However, utilizing a massive volume of high spatial and temporal resolution data for ecological applications is challenging. One approach is to employ composite images generated from the multi-temporal satellite data. The research was conducted in two study sites located in the Indochinese Peninsula, Laos-Thailand and Vietnam-Cambodia, vulnerable to deforestation and land use changes. We assessed the potential of recently available composite images, such as Biophysical Image Composite (BIC), Forest Cover Composite (FCC), Enhanced Forest Cover Composite (EFCC), and Water Cover Composite (WCC) for the classification and mapping of land cover types. Three machine learning classifiers, k-Nearest Neighbors (KNN), Support Vector Machines (SVM) and Random Forests (RF) were employed and the performance of composite images was evaluated quantitatively with the support of ground truth data. The overall accuracies (Kappa coefficient) obtained from the combination of composite images were 0.92 (0.89) and 0.90 (0.86) for Laos-Thailand, and Vietnam sites respectively. These results highlight effectiveness of the composite images for the classification and mapping of land cover types.

关键词： Satellite Remote Sensing Machine Learning Optical Radar

Assessing the Vulnerability of Agricultural Production Activities in the Coastal Area of Thua Thien Hue Province, Vietnam by GIS Analysis Tools

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Journal of Geoscience and Environment Protection 2019年第5期7卷 116-130页

作者： nguyen hoang Son Dao Dinh Cham Phan Anh Hang nguyen Trong Quan nguyen thanh hoan University of Education Hue University Hue Vietnam Institute of Geography Vietnam Academy of Science and Technology Hanoi Vietnam Graduate University of Science and Technology Vietnam Academy of Science and Technology Hanoi Vietnam University of Science Hue University Hue Vietnam

Using Geography Information System (GIS) tools and remote sensing data in assessing the level of vulnerability of agricultural production activities in the coastal area has become more efficient in the recent years. This research has identified the sensitivity index (S) (including the traffic access index;the impact of residential areas;the impacts of industrial zones;the community dependence level), exposure index (E) (the sea level rises to 2100;the temperature change to 2100), the adaptable capacity index (AC) (slope;morphology), thereby synthesizing the vulnerability index (V). Based on the indexes to calculate vulnerability, the high to very high vulnerability area is 37,081.44 ha, accounting for 68.09%;the average vulnerability level is 15,286.49 ha, accounting for 28.07%;the low to very low vulnerability level with an area is 2087.82 ha, accounting for 3.84% of the total area. With a high and very high vulnerability accounting for 68.09%, there will be great influences on the lives of resident in the area, especially agricultural production.

关键词： Vulnerability Indexes GIS Tools Agricultural Production Activity Coastal Areas

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

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Journal of Geographic Information System 2013年第4期5卷 369-377页

作者： Bayan Alsaaideh Ahmad Al-Hanbali Ryutaro Tateishi Toshiyuki Kobayashi nguyen thanh hoan Center for Environmental Remote Sensing (CEReS) Chiba University Chiba Japan

A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

关键词： Mangrove Forests NDWI NDVI DEM Japan

A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

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Advances in Remote Sensing 2014年第3期3卷 208-218页

作者： Lan Mi nguyen thanh hoan Ryutaro Tateishi Kotaro Iizuka Bayan Alsaaideh Toshiyuki Kobayashi Center for Environmental Remote Sensing Chiba University Chiba Japan Institute of Geography Vietnam Academy of Science and Technology (VAST) Hanoi Vietnam

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

关键词： Slope Correction Land Cover Classification Feature Selection Sequential Minimal Optimization Random Forest

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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Computers, Materials & Continua 2024年第2期78卷 2181-2205页

作者： thanh-Lam nguyen HaoKao thanh-Tuan nguyen Mong-Fong Horng Chin-Shiuh Shieh Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly *** the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the *** applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the ***,malicious threats on the internet are on the rise as *** Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet *** scale and complexity of DDoS attacks are constantly *** Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those *** addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant ***,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown *** attacks,which are not encountered during the system’s training,can lead to misclassification with significant *** this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional *** proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)***,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)*** success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering *** has yielded a novel method in the field of unknown attack *** further expands the trend of applying DL and M

关键词： Cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019

GLOBAL BOUND ON THE GRADIENT OF SOLUTIONS TO p-LAPLACE TYPE EQUATIONS WITH MIXED DATA

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Acta Mathematica Scientia 2024年第4期44卷 1394-1414页

作者： Minh-Phuong TRAN The-Quang TRAN thanh-Nhan nguyen Applied Analysis Research Group Faculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam nguyen Huu Huan High School Thu Duc CityVietnam Group of Analysis and Applied Mathematics Department of MathematicsHo Chi Minh City University of EducationVietnam

In this paper,the study of gradient regularity for solutions of a class of elliptic problems of p-Laplace type is *** particular,we prove a global result concerning Lorentz-Morrey regularity of the non-homogeneous boundary data problem:-div((s^(2)+|▽u|^(2)p-2/2)▽u)=-div(|f|^(p-2)f)+g inΩ,u=h in■Ω,with the(sub-elliptic)degeneracy condition s∈[0,1]and with mixed data f∈L^(p)(Q;R^(n)),g∈Lp/(p-1)(Ω;R^(n))for p∈(1,n).This problem naturally arises in various applications such as dynamics of non-Newtonian fluid theory,electro-rheology,radiation of heat,plastic moulding and many *** on the idea of level-set inequality on fractional maximal distribution functions,it enables us to carry out a global regularity result of the solution via fractional maximal *** to the significance of M_(α)and its relation with Riesz potential,estimates via fractional maximal functions allow us to bound oscillations not only for solution but also its fractional derivatives of orderα.Our approach therefore has its own interest.

关键词： gradient estimates p-Laplace quasilinear elliptic equation fractional maximal operators Lorentz-Morrey spaces