• 한국초지조사료학회지
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• 제43권1호
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• pp.11-21
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• 2023

본 연구는 기계학습을 기반으로 제작한 수량예측모델을 이용하여 PCR 4.5 시나리오에 따른 사일리지용 옥수수(WCC)의 피해량 산정 및 전자지도를 작성할 목적으로 수행하였다. WCC 데이터는 수입적응성 시험보고서(n=1,219), 국립축산과학원 시험연구보고서(n=1,294), 한국축산학회지(n=8), 한국초지조사료학회지(n=707) 및 학위논문(n=4)에서 총 3,232점을 수집하였으며, 기상데이터는 기상청의 기상자료개방포털에서 수집하였다. 본 연구에서 이상기상에 따른 WCC의 피해량은 RCP 4.5 시나리오에 따른 월평균기온 및 강수량을 시간단위로 환산하여 준용하여 산정하였다. 정상기상에서 DMY 예측값은 13,845~19,347 kg/ha 범위로 나타났다. 이상기상에 따른 피해량은 이상기온 2050 및 2100년 각각 -263~360 및-1,023~92 kg/ha, 이상강수량 2050 및 2100년 각각 -17~-2 및-12~2 kg/ha였다. 월평균기온이 증가함에 따라서 WCC의 DMY는 증가하는 경향으로 나타났다. RCP 4.5 시나리오를 통해 산정한 WCC의 피해량은 QGIS를 이용하여 전자지도로 제시하였다. 본 연구는 온실가스 저감이 진행된 시나리오를 이용했지만, 추가 연구는 온실가스 저감이 되지 않은 RCP 시나리오를 이용한 연구를 수행할 필요가 있다.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.57-66
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• 2023

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.

• 인간식물환경학회지
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• 제24권5호
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• pp.509-517
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• 2021

Background and objective: Problems currently occurred in the environment are caused by a lack of environmental awareness of the community. Biology students learn the environment in ecological learning. Students must explore environmental knowledge (EK) and are expected to have high environmental awareness (EA) and then apply their knowledge in daily life with pro-environmental behavior (PEB). There is a need for designing climate-change book serials for university students (CCBS-US) towards contextualized learning. This study aimed to determine the relationship between EK and EA and the PEB of biology students as a baseline for designing CCBS-US. Methods: The study used a quantitative descriptive method with a correlational design. Total 136 biology students from a state university in Jakarta, Indonesia served as samples of the study. Results: Research results revealed a positive relationship between EK and PEB of biology students. There was a positive relationship between EA and PEB of biology students and between EK and EA and PEB of Biology students. Moreover, 77% of students reported their EK is very high, 55% of students reported their EA is medium, and 46% of students reported their PEB is medium. Ecological learning has a vital role in shaping the EK, EA, and PEB, thus CCBS-US needs to be developed. Conclusion: The study concluded that there was a relationship between EK, EA, and PEB. The study recommends the development of CCBS-US based on the survey results.

• 한국지구과학회지
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• 제28권4호
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• pp.445-452
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• 2007

고등학교 지구과학 I '날씨와 기후' 단원 중심으로 웹 기반 프로젝트 수업을 적용하였다. 연구 대상은 고등학교 2학년 4개 학급이었다. 자기 주도적으로 설계된 웹기반 프로젝트 수업이 학생들의 자기 주도적 학습력, 자아개념 및 학교에 대한 태도에 긍정적인 영향을 주었다. 웹 기반 프로젝트 수업의 결과, 자기 주도적 학습력 '행동통제' 변인을 제외한 7개 변인, 자아개념 3개 변인, 학교에 대한 태도 2개 변인이 전통적인 수업과 유의미한 차이를 보였다. 또한 모든 수준의 학습자에게 웹 기반 프로젝트 수업이 자기 주도적 학습과정에 긍정적인 영향을 준다는 결과를 얻을 수 있었다.

• 한국물환경학회지
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• 제40권3호
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• pp.121-129
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• 2024

Harmful cyanobacterial blooms (HCBs) are caused by the rapid proliferation of cyanobacteria and are believed to be exacerbated by climate change. However, the extent to which HCBs will be stimulated in the future due to increased temperature remains uncertain. This study aims to predict the future occurrence of cyanobacteria in the Nakdong River, which has the highest incidence of HCBs in South Korea, based on temperature rise scenarios. Representative Concentration Pathways (RCPs) were used as the basis for these scenarios. Data-driven model simulations were conducted, and out of the four machine learning techniques tested (multiple linear regression, support vector regressor, decision tree, and random forest), the random forest model was selected for its relatively high prediction accuracy. The random forest model was used to predict the occurrence of cyanobacteria. The results of boxplot and time-series analyses showed that under the worst-case scenario (RCP8.5 (2100)), where temperature increases significantly, cyanobacterial abundance across all study areas was greatly stimulated. The study also found that the frequencies of HCB occurrences exceeding certain thresholds (100,000 and 1,000,000 cells/mL) increased under both the best-case scenario (RCP2.6 (2050)) and worst-case scenario (RCP8.5 (2100)). These findings suggest that the frequency of HCB occurrences surpassing a certain threshold level can serve as a useful diagnostic indicator of vulnerability to temperature increases caused by climate change. Additionally, this study highlights that water bodies currently susceptible to HCBs are likely to become even more vulnerable with climate change compared to those that are currently less susceptible.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• 한국초지조사료학회지
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• 제41권4호
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• pp.287-294
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• 2021

본 연구는 기계학습을 통한 수량예측모델을 이용하여 이상기상에 따른 WCM의 DMY 피해량을 산출하기 위한 목적으로 수행하였다. 수량예측모델은 WCM 데이터 및 기상 데이터를 수집 후 가공하여 8가지 기계학습을 통해 제작하였으며 실험지역은 경기도로 선정하였다. 수량예측모델은 기계학습 기법 중 정확성이 가장 높은 DeepCrossing (R²=0.5442, RMSE=0.1769) 기법을 통해 제작하였다. 피해량은 정상기상 및 이상기상의 DMY 예측값 간 차이로 산출하였다. 정상기상에서 WCM의 DMY 예측값은 지역에 따라 차이가 있으나 15,003~17,517 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 DMY 예측값은 지역 및 각 이상기상 수준에 따라 차이가 있었으며 각각 14,947~17,571 kg/ha, 14,986~17,525 kg/ha 및 14,920~17,557 kg/ha 범위로 나타났다. 이상기온, 이상강수량 및 이상풍속에서 WCM의 피해량은 각각 -68~89 kg/ha, -17~17 kg/ha 및 -112~121 kg/ha 범위로 피해로 판단할 수 없는 수준이었다. WCM의 정확한 피해량을 산출하기 위해서는 수량예측모델에 이용하는 이상기상 데이터 수의 증가가 필요하다.

• 한국초등과학교육학회지:초등과학교육
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• 제32권1호
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• pp.82-94
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• 2013

The purpose of this study was to investigate the process and the effect of pre-service teacher's scaffolding in environmental camp program about global climate change. For this study, developed the environmental camp program based pre-service teacher's scaffolding and applied to 78 $5^{th}$ students. We analyzed the role of pre-service teacher in the process of scaffolding. In the result, the pre-service teachers conducted cognitive scaffolding like as "Focus", "Hint", "Tell or Summarize" and "Technical Help". They carried out the emotional scaffolding like as "Create Cheerful Atmosphere", "Encourage", and "Help in Living". Teaching and learning about global climate change, the theme of the camp, was regarded uncertain and complex. So, pre-service teacher's scaffolding was effective to promote environmental literacy about climate change of primary students (<0.05). The student teachers understanded the characteristics of the children through emotionally close relationships. The primary students were learned easier about global climate change through cognitive and emotional scaffolding. They experienced environmental practice with communal living in camp.

• 한국지구과학회지
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• 제39권4호
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• pp.378-387
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• 2018

In this research, the change of Korean middle-school science education environments is investigated through analyzing eighth graders' survey data collected over the past 20 years of TIMSS. We extracted educational context variables that provide meaningful information on changes of Korean science education, and have been surveyed more than 3 study cycles up to TIMSS 2015. The selected educational context variables include school resources and school climate from the school principal's questionnaires, and teacher characteristics and instructional activities from the teacher's questionnaires. For each context variable, we analyzed its trend over TIMSS cycles, and discussed its implications in light of Korean educational policy and curriculum changes. Based on the results, we recommended several ways that help to improve science teaching and learning in light of lab assistants, computer availability, teacher learning community, and middle school Earth science curriculum.

• 한국정보시스템학회지:정보시스템연구
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• 제33권1호
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• pp.31-44
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• 2024

Purpose The purpose of this study is to use climate data to find the algorithm with the highest Hallabong production prediction ability and to predict future Hallabong production in areas where Hallabong cultivation is expected to be possible. Design/methodology/approach The research is conducted in two stages. In the first step, find the algorithm with the highest predictive power among XGBoost, Random Forest, SVM, and LSTM methodologies. In the second stage, the algorithm found in the first stage is applied to predict future Hallabong production in three regions where Hallabong production is expected to be possible. Findings As with many prediction studies, we found that XGBoost showed the highest prediction power. Even in areas where Hallabong production is expected to be possible, Hallabong production was predicted to be highest in Hongcheon, Gangwon-do, which has the highest latitude.