Abstract

dverse weather conditions regularly lead to severe congestion and large travel time delays on road networks all over the world. Different climate scenarios indicate that in the future adverse weather conditions are likely to become more frequent, last longer and will be more extreme. Although climate mitigation measures are being taken, it remains important to investigate how adverse weather events will affect the performance of the road network in the future. The main objective of this paper is to give an overview of how the impact of adverse weather conditions and adaptation measures on road network performance can be quantified. A literature review has been performed to show what is empirically known about the impact of adverse weather conditions on the road network performance. Furthermore, available methods to quantify the impact of adverse weather conditions and adaptation measures on the road network performance for future situations are reviewed. As an example, a case study for the municipality of Rotterdam has been carried out that shows how a combination of models can be used to analyse which links in the road network are most vulnerable for increasingly severe local weather related disturbances. The results of the case study allow local authorities to decide whether or not they need to take adaptation measures. © 2016, Editorial Board EJTIR. All rights reserved.

Abstract

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.

Abstract

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.