• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.985-986
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• 2005

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.47-57
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• 2018

This study implemented the rainfall-runoff analysis of the Mekong River basin using the SWAT (Soil and Water Assessment Tool). The runoff analysis was simulated for 2000~2007, and 11 parameters were calibrated using the SUFI-2 (Sequential Uncertainty Fitting-version 2) algorithm of SWAT-CUP (Calibration and Uncertainty Program). As a result of analyzing optimal parameters and sensitivity analysis for 6 cases, the parameter ALPHA_BF was found to be the most sensitive. The reproducibility of the rainfall-runoff results decreased with increasing number of stations used for parameter calibration. The rainfall-runoff simulation results of Case 6 showed that the RMSE of Nong Khai and Kratie stations were 0.97 and 0.9, respectively, and the runoff patterns were relatively accurately simulated. The runoff patterns of Mukdahan and Khong Chaim stations were underestimated during the flood season from 2004 to 2005 but it was acceptable in terms of the overall runoff pattern. These results suggest that the combination of SWAT and SWAT-CUP models is applicable to very large watersheds such as the Mekong for rainfall-runoff simulation, but further studies are needed to reduce the range of modeling uncertainty.

• Water for future
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• v.46 no.6
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• pp.28-33
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• 2013

• Corrosion Science and Technology
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• v.7 no.6
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• pp.328-331
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• 2008

Corrosion behaviour of water intake gate steel structures with different protective measures was investigated. Five material alternatives were taken for investigation, including: imported and recycled stainless steel, carbon steel with hot zinc spraying, painting and composite coatings. Results of corrosion rate for carbon steel, SUS 304, hot zinc spray coats in three water systems of Mekong river basin (saline, blackish and fresh) were also presented. Corrosion rate of carbon steel decreased with decreasing salinity in the investigated water environments. Meanwhile, these values for zinc coated steel, behaved by another way. Environmental data for these systems were filed and discussed in relation with corrosion characteristics. Method of Life Cycle Assessment (LCA) was applied in materials selection for water intake gate construction. From point of Life Cycle Cost (LCA) the following ranking was obtained: Zinc sprayed steel < Recycled stainless steel < Composite coated steel < Painting steel < SUS 304 From investigated results, hot zinc spray coating has been applied as protective measure for steel structures of water intake systems in Mekong river basin.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.441-441
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• 2002

In order to ensure a balance between economic development and a healthy Mekong Basin environment supporting natural resources diversity and productivity critical to the livelihood of its 65 million inhabitants, the Mekong River Commission (MRC) has been investigating the use of radar to remotely characterize and monitor the diversity, complexity, size and connectivity of the Basin's aquatic habitats. The PACRIM AIRSAR Mission provided an opportunity to evaluate the usefulness of radar technology to derive information for assessing, forecasting and mitigating possible cumulative and long-term impacts of development on the natural environment and the people's livelihood. This paper presents the results of mapping wetland cover types using multi-polarimetric radar for an area of the north-western corner of the Tonle Sap basin with data acquired from the AIRSAR Mission in September 2000. The implementation of a newly developed segmentation classification routine used to derive the image classification is described and the results of a fieldwork campaign to check the classification is presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.173-173
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• 2023

The Mekong River Basin (MRB) is a crucial watershed in Asia, impacting over 60 million people across six developing nations. Accurate satellite-based precipitation products (SPPs) are essential for effective hydrological and watershed management in this region. However, the performance of SPPs has been varied and limited. The APHRODITE product, a unique gauge-based dataset for MRB, is widely used but is only available until 2015. In this study, we present a novel framework for correcting SPPs in the MRB by employing a deep learning approach that combines convolutional neural networks and encoder-decoder architecture to address pixel-by-pixel bias and enhance accuracy. The DLF was applied to four widely used SPPs (TRMM, CMORPH, CHIRPS, and PERSIANN-CDR) in MRB. For the original SPPs, the TRMM product outperformed the other SPPs. Results revealed that the DLF effectively bridged the spatial-temporal gap between the SPPs and the gauge-based dataset (APHRODITE). Among the four corrected products, ADJ-TRMM demonstrated the best performance, followed by ADJ-CDR, ADJ-CHIRPS, and ADJ-CMORPH. The DLF offered a robust and adaptable solution for bias correction in the MRB and beyond, capable of detecting intricate patterns and learning from data to make appropriate adjustments. With the discontinuation of the APHRODITE product, DLF represents a promising solution for generating a more current and reliable dataset for MRB research. This research showcased the potential of deep learning-based methods for improving the accuracy of SPPs, particularly in regions like the MRB, where gauge-based datasets are limited or discontinued.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.682-686
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• 2003

This paper is concerned with 'reverse difference' of marine sediments at the Camau tongue in the extreme south of Vietnam. We demonstrate the importance of remote sensing in geomorphology and marine geological application, using only visual evaluation and some data-processing techniques. In this paper, about 10,000 km$^2$ of the territorial water in the extreme south of Vietnam is being studied. We show that form and behavior of Mekong and its branch can be determined by visually interpreting remote sensing images and using ERDAS IMAGE 8.5 software. Besides, the 'reverse difference' phenomenon is explained by flows of Mekong river and its branches.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1095-1107
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• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.