http://www.colloquiam.com/wd/index.php?action=history&feed=atom&title=Ahn%2A_Lee_2024a Ahn* Lee 2024a - Revision history 2026-05-11T05:48:02Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 http://www.colloquiam.com/wd/index.php?title=Ahn*_Lee_2024a&diff=301303&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 729462013 to Ahn* Lee 2024a 2024-06-06T14:24:15Z <p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_729462013" class="mw-redirect" title="Draft Sanchez Pinedo 729462013">Draft Sanchez Pinedo 729462013</a> to <a href="/public/Ahn*_Lee_2024a" title="Ahn* Lee 2024a">Ahn* Lee 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 14:24, 6 June 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table> JSanchez http://www.colloquiam.com/wd/index.php?title=Ahn*_Lee_2024a&diff=301302&oldid=prev JSanchez at 14:24, 6 June 2024 2024-06-06T14:24:11Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:24, 6 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Climate change, such as increase in CO2 levels and rising temperatures, can have a significant impact on paddy rice production and increase the uncertainty of yield forecasts. This study aims to employ AI modeling for forecasting paddy rice yield and present the findings of a quantitative analysis to determine its ability to generate stable forecasts under extreme weather conditions, such as heatwaves, low temperatures, and heavy rainfall. Vegetation growth indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product were utilized. These indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FPAR), and Near-Infrared Reflectance of vegetation (NIRv). Meteorological variables such as downward solar radiation flux, daily temperature difference, precipitation, relative humidity, and temperature were also used. Over 23 years of experimentation (2000-2022), yields under extreme weather conditions did not exhibit a significant difference from the normal period, with a Mean Absolute Error (MAE) ranging from 0.30 to 0.33 ton/ha, representing a 4-5% error of the average yield. This study presents an AI modeling methodology that enables stable predictions of paddy rice yields, even under extreme weather conditions. Future work should focus on refining input data and optimizing the model by analyzing cases of extreme weather.</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Climate change, such as increase in CO2 levels and rising temperatures, can have a significant impact on paddy rice production and increase the uncertainty of yield forecasts. This study aims to employ AI modeling for forecasting paddy rice yield and present the findings of a quantitative analysis to determine its ability to generate stable forecasts under extreme weather conditions, such as heatwaves, low temperatures, and heavy rainfall. Vegetation growth indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product were utilized. These indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FPAR), and Near-Infrared Reflectance of vegetation (NIRv). Meteorological variables such as downward solar radiation flux, daily temperature difference, precipitation, relative humidity, and temperature were also used. Over 23 years of experimentation (2000-2022), yields under extreme weather conditions did not exhibit a significant difference from the normal period, with a Mean Absolute Error (MAE) ranging from 0.30 to 0.33 ton/ha, representing a 4-5% error of the average yield. This study presents an AI modeling methodology that enables stable predictions of paddy rice yields, even under extreme weather conditions. Future work should focus on refining input data and optimizing the model by analyzing cases of extreme weather.</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_729462013302.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:301300:newid:301302 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Ahn*_Lee_2024a&diff=301300&oldid=prev JSanchez at 14:24, 6 June 2024 2024-06-06T14:24:09Z <p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:24, 6 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Climate change, such as increase in CO2 levels and rising temperatures, can have a significant impact on paddy rice production and increase the uncertainty of yield forecasts. This study aims to employ AI modeling for forecasting paddy rice yield and present the findings of a quantitative analysis to determine its ability to generate stable forecasts under extreme weather conditions, such as heatwaves, low temperatures, and heavy rainfall. Vegetation growth indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite product were utilized. These indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FPAR), and Near-Infrared Reflectance of vegetation (NIRv). Meteorological variables such as downward solar radiation flux, daily temperature difference, precipitation, relative humidity, and temperature were also used. Over 23 years of experimentation (2000-2022), yields under extreme weather conditions did not exhibit a significant difference from the normal period, with a Mean Absolute Error (MAE) ranging from 0.30 to 0.33 ton/ha, representing a 4-5% error of the average yield. This study presents an AI modeling methodology that enables stable predictions of paddy rice yields, even under extreme weather conditions. Future work should focus on refining input data and optimizing the model by analyzing cases of extreme weather.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:301299:newid:301300 --> </table> JSanchez http://www.colloquiam.com/wd/index.php?title=Ahn*_Lee_2024a&diff=301299&oldid=prev JSanchez: Created blank page 2024-06-06T14:24:07Z <p>Created blank page</p> <p><b>New page</b></p><div></div> JSanchez