Quantitative Flood Damage Evaluation Using Grid-Based Spatial Analysis Data (original) (raw)
Related papers
Assessment of Water Resources Vulnerability Index by Nation
Journal of Korea Water Resources Association, 2014
Discussions for water resources vulnerability and index development with sustainable concept are actively being made in recent years. Based on such index, water resources vulnerability of present and future is determined and diagnosed. This study calculated the water resources vulnerability rankings by 152 nations, using indicator related to water resources assessment that can be obtained from World Bank, VRI (Vulnerability Resilience Indicator), ESI (Environmental Sustainability Index). In order to quantitatively assess of water resources vulnerability based on this indicator, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) technique was applied to index water vulnerability and to determine the rankings by nations. As a results, South Korea was ranked as the 88th among the 152 nations including Korea. Among the continents, Oceania was the least vulnerable and Afirica was the most vulnerable in continents. WUnited State, Japan, Korea and China were vulnerable in order among the major countries. Therefore, water resources vulnerability rankings by nations in this study helps us to better understand the situation of South Korea and provide the data for water resources planning and measure.
Prediction of River Water Level Using Deep-Learning Open Library
Korean Society of Hazard Mitigation, 2017
This research aims to predict the water level using deep-learning algorithm. To achieve this goal, we applied the TensorFlow, a deep-learning open source library to predict the water level of Okcheon station in the Guem-river. For model training and prediction, two hourly water level data sets of the 3 water level stations (Sutong, Hotan, Songcheon) are prepared: from 2002 to 2013 (training); from 2014 to 2016 (prediction). Even if many of physical data are necessary to understand water cycle system, in particular, model rainfall-runoff process, we used only upstream observed water level information to predict downstream water level using multi-linear regression and Long Short-Term Memory(LSTM) models based on the TensorFlow. The results showed that the weights(or regression coefficients) of multi-linear regression model were very fluctuated due to training trials, then, the predicted high water level were too much underestimated than the observations. On the other hand, the LSTM model predicted the downstream water level very stably regardless of water level height for the study period because sequence length of the LSTM memorize antecedent water level information for model training and updating.
2020
Based on monthly average groundwater recharge over a nearly 10 year period, results of fully integrated hydrologic modeling of SWAT-MODFLOW, land cover, land use, soil type and hydrologic response unit (HRU) was used to assess the dominant influencing factors of groundwater recharge spatial patterns in Jangseong district. As dominant factors, land cover was FRSE (forest-evergreen) and soil type was Samgag. Landsat-8 OLI imaging spectrometer data were acquired in the period 2003 to 2004 and seasonal bare soil lines (BSL) were estimated through NIR-RED plot. Extent of slope of BSL was from 1.092 to 1.343 and the intercept was from -0.004 to -0.015. To know correlation between spatial groundwater recharge and soil-vegetation indices (PVI, NDVI, NDTI, NDRI), this study employed frequency and regression analysis. On May, RED band increased up 3 to 4 times compared to other seasons and only one turning point appeared as recharge-index with upward parabola bell shape as results of existing...
Journal of Rainwater Catchment Systems, 2015
Groundwater resources at Dalad, Inner Mongolia were investigated by groundwater metering in 2013 as supplementary investigation during 2002-2006. Since Dalad is located in the middle reach area of the Yellow River basin and classified as arid or semi-arid area with less than 360mm precipitation and more than 2,200mm potential evaporation, the river water is not sufficiently supplied to the area and therefore, irrigation and industrial waters inevitably depend on groundwater resources. As a result of the investigation, groundwater levels at almost of all the observation wells have descended remarkably since 2005 and especially the decreases in the southern part of the objective area are severe by a range of 0.8-5.4m. The groundwater amount in the southern part in 2013 is roughly estimated to be 2.91×10 7 m 3 , which proves a distinct downward trend. According to the autoregressive-like forecasts, including the Holt-Winters method, the unconfined groundwater in the southern part would be unavailable in the middle of 2030's. The groundwater amount of the objective area is also decreasing severely and the increase in irrigation and industrial waters must be its potential reasons, except the decrease in the precipitation during 2005-2013. The change of the groundwater usage and flows might decrease the discharge of the Yellow River through the groundwater seepage and it is not only a water resources problem in this area, but also in the basin scale. Therefore, the groundwater management in this area is a pressing issue and should be done by local government or some water-utilization association against both irrigation water and industry water users. The water resources are inherently including uncertainty, so continuing observation is essential to maintain the water resources at Dalad.