The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 위성에서 얻은 지구관측데이터(EOD)를 활용하여 후지산 전망에 대한 예측 방법을 제시합니다. 우리는 고정점 관측으로 생성된 사진 데이터를 기반으로 특정 날짜에 산이 얼마나 잘 보이는지 나타내는 후지산 전망 지수(FVI)를 정의했습니다. 0~30일 범위의 FVI 장기 예측변수는 기후 및 지구 관측 데이터에 대한 지원 벡터 머신 회귀를 통해 구축되었습니다. 에어로졸 질량 농도(AMC)는 예측 성능을 향상시키며, 이러한 성능은 장기적 범위에서 특히 중요하다는 것이 밝혀졌습니다.
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부
Mitsuru KAKIMOTO, Hisaaki HATANO, Yosoko NISHIZAWA, "Forecasting the View of Mt. Fuji Using Earth Observation Data" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 8, pp. 1551-1560, August 2009, doi: 10.1587/transinf.E92.D.1551.
Abstract: In this paper, we present a forecasting method for the view of Mt. Fuji as an application of Earth observation data (EOD) obtained by satellites. We defined the Mt. Fuji viewing index (FVI) that characterises how well the mountain looks on a given day, based on photo data produced by a fixed-point observation. A long-term predictor of FVI, ranging from 0 to 30 days, was constructed through support vector machine regression on climate and earth observation data. It was found that the aerosol mass concentration (AMC) improves prediction performance, and such performance is particularly significant in the long-term range.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1551/_p
부
@ARTICLE{e92-d_8_1551,
author={Mitsuru KAKIMOTO, Hisaaki HATANO, Yosoko NISHIZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Forecasting the View of Mt. Fuji Using Earth Observation Data},
year={2009},
volume={E92-D},
number={8},
pages={1551-1560},
abstract={In this paper, we present a forecasting method for the view of Mt. Fuji as an application of Earth observation data (EOD) obtained by satellites. We defined the Mt. Fuji viewing index (FVI) that characterises how well the mountain looks on a given day, based on photo data produced by a fixed-point observation. A long-term predictor of FVI, ranging from 0 to 30 days, was constructed through support vector machine regression on climate and earth observation data. It was found that the aerosol mass concentration (AMC) improves prediction performance, and such performance is particularly significant in the long-term range.},
keywords={},
doi={10.1587/transinf.E92.D.1551},
ISSN={1745-1361},
month={August},}
부
TY - JOUR
TI - Forecasting the View of Mt. Fuji Using Earth Observation Data
T2 - IEICE TRANSACTIONS on Information
SP - 1551
EP - 1560
AU - Mitsuru KAKIMOTO
AU - Hisaaki HATANO
AU - Yosoko NISHIZAWA
PY - 2009
DO - 10.1587/transinf.E92.D.1551
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2009
AB - In this paper, we present a forecasting method for the view of Mt. Fuji as an application of Earth observation data (EOD) obtained by satellites. We defined the Mt. Fuji viewing index (FVI) that characterises how well the mountain looks on a given day, based on photo data produced by a fixed-point observation. A long-term predictor of FVI, ranging from 0 to 30 days, was constructed through support vector machine regression on climate and earth observation data. It was found that the aerosol mass concentration (AMC) improves prediction performance, and such performance is particularly significant in the long-term range.
ER -