Comparative Influences of Precipitation and River Stage on Groundwater Levels in Near-River Areas

Kim, In-Cheol; Park, Donggyu; Kyung, Doohyun; Kım, Garam; Kim, Sun-Bin; Lee, Jun-Hwan

doi:10.3390/su8010001

Cited by 52 publications

(12 citation statements)

References 16 publications

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“…Air pollution is caused by acid rain and it leads to air pollution [91]. Air pollution and eutrophication are the major reasons for water pollution.…”

Section: Life Cycle Impact Assessment (Lcia)mentioning

confidence: 99%

Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic

Oğuz

Şentürk

2019

Sustainability

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Energy production without destroying the environment has been one of the most crucial issues for people living in today’s world. In order to analyze whole environmental and/or economic impacts of the energy production process, life cycle assessment (LCA) and life cycle cost (LCC) are widely used. In this study, two distinct renewable energy systems are assessed. First, a land-based wind farm, which has been operating in Bozcaada Island since 2000, is compared to a proposed solar photovoltaic power plant in terms of Energy Pay-Back Time (EPBT) periods and greenhouse gas (GHG) emissions and life cycle cost. The energy production process including the recycling phase evaluated “from cradle to grave” using GaBi software for both cases. All scenarios are compared by considering different impact categories such as global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP). Following this, levelized unit cost to produce 1 MWh electricity (LUCE) is calculated for both systems. This study revealed that LCA and LCCA are useful and practical tools that help to determine drawbacks and benefits of different renewable energy systems considering their long-term environmental and economic impacts. Our findings show that onshore wind farms have a number of benefits than proposed photovoltaic power plants in terms of environmental and cost aspects.

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“…Air pollution is caused by acid rain and it leads to air pollution [91]. Air pollution and eutrophication are the major reasons for water pollution.…”

Section: Life Cycle Impact Assessment (Lcia)mentioning

confidence: 99%

Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic

Oğuz

Şentürk

2019

Sustainability

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“…Replication and noise data are removed and the analysis accuracy is improved by selecting only valid data. Next, we extract a summary, which is a common component of both patents and papers, and then we structure the summary as a document-term matrix (DTM) using text mining [28].…”

Section: Proposed Methodology For Keyword Extractionmentioning

confidence: 99%

Patent Keyword Extraction for Sustainable Technology Management

Kim

Choi²,

Park

et al. 2018

Sustainability

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Recently, sustainable growth and development has become an important issue for governments and corporations. However, maintaining sustainable development is very difficult. These difficulties can be attributed to sociocultural and political backgrounds that change over time [1]. Because of these changes, the technologies for sustainability also change, so governments and companies attempt to predict and manage technology using patent analyses, but it is very difficult to predict the rapidly changing technology markets. The best way to achieve insight into technology management in this rapidly changing market is to build a technology management direction and strategy that is flexible and adaptable to the volatile market environment through continuous monitoring and analysis. Quantitative patent analysis using text mining is an effective method for sustainable technology management. There have been many studies that have used text mining and word-based patent analyses to extract keywords and remove noise words. Because the extracted keywords are considered to have a significant effect on the further analysis, researchers need to carefully check out whether they are valid or not. However, most prior studies assume that the extracted keywords are appropriate, without evaluating their validity. Therefore, the criteria used to extract keywords needs to change. Until now, these criteria have focused on how well a patent can be classified according to its technical characteristics in the collected patent data set, typically using term frequency-inverse document frequency weights that are calculated by comparing the words in patents. However, this is not suitable when analyzing a single patent. Therefore, we need keyword selection criteria and an extraction method capable of representing the technical characteristics of a single patent without comparing them with other patents. In this study, we proposed a methodology to extract valid keywords from single patent documents using relevant papers and their authors' keywords. We evaluated the validity of the proposed method and its practical performance using a statistical verification experiment. First, by comparing the document similarity between papers and patents containing the same search terms in their titles, we verified the validity of the proposed method of extracting patent keywords using authors' keywords and the paper. We also confirmed that the proposed method improves the precision by about 17.4% over the existing method. It is expected that the outcome of this study will contribute to increasing the reliability and the validity of the research on patent analyses based on text mining and improving the quality of such studies.

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“…The effect of preceding precipitation and time lag on NGWA.org I. Kim and J. Lee Groundwater 60, no. 3: 344-361 GWL can be effectively reflected and quantified using the autoregressive (AR) and MA (Wilhite and Glantz 1985;Guttman 1999;Yang and Kim 2011;Kim et al 2016).…”

Section: Correlation Analysis: Time Series Analysesmentioning

confidence: 99%

Performance Analysis ofANNPrediction for Groundwater Level ConsideringRegional‐SpecificInfluence Components

Kim

Lee²

2021

Groundwater

Self Cite

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Groundwater level (GWL) varies periodically or non-periodically with various factors including precipitation, river stage (RS) change, sea level, and dewatering activities. In this study, the effect of influence components on the prediction of GWL using an artificial neural network (ANN) was investigated. Six regions with different hydrologic and geologic conditions were collected and adopted in the investigation using various input combinations. In urban areas with a high surface paved ratio, GWL was mainly affected by RS. In rural areas, the permeability of ground showed a significant impact on GWL. For such cases, the moving average (MA) was a suitable component as it could reflect both time lag and the effect of preceding precipitation. It was shown that site-specific influence component should be firstly identified and introduced into input for more enhanced and reliable prediction of GWL using ANN. The effect of learning data length (LDL) was less significant. In urban and rural areas, the introduction of RS and MA into ANN input significantly improved the prediction performance, respectively, which was consistent with the correlation analysis of GWL influence components.

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Comparative Influences of Precipitation and River Stage on Groundwater Levels in Near-River Areas

Cited by 52 publications

References 16 publications

Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic

Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic

Patent Keyword Extraction for Sustainable Technology Management

Performance Analysis ofANNPrediction for Groundwater Level ConsideringRegional‐SpecificInfluence Components

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