TY  - JOUR
AB  - Myocardial infarctions (MIs) are a major cause of death worldwide, and both high and low temperatures (i.e. heat and cold) may increase the risk of MI. The relationship between health impacts and climate is complex and influenced by a multitude of climatic, environmental, sociodemographic and behavioural factors. Here, we present a machine learning (ML) approach for predicting MI events based on multiple environmental and demographic variables. We derived data on MI events from the KORA MI registry dataset for Augsburg, Germany, between 1998 and 2015.Multivariable predictors include weather and climate, air pollution (PM10, NO, NO2, SO2 and O3), surrounding vegetation and demographic data. We tested the following ML regression algorithms: decision tree, random forest, multi-layer perceptron, gradient boosting and ridge regression. The models are able to predict the total annual number of MIs reasonably well (adjusted R2 = 0.62–0.71). Inter-annual variations and long-term trends are captured. Across models the most important predictors are air pollution and daily temperatures. Variables not related to environmental conditions, such as demographics need to be considered as well. This ML approachprovides a promising basis to model future MI under changing environmental conditions, as projected by scenarios for climate and other environmental changes.
AU  - Marien, L.*
AU  - Valizadeh, M.
AU  - zu Castell, W.*
AU  - Nam, C.*
AU  - Rechid, D.*
AU  - Schneider, A.E.
AU  - Meisinger, C.
AU  - Linseisen, J.
AU  - Wolf, K.
AU  - Bouwer, L.M.*
C1  - 66217
C2  - 52760
SP  - 3015-3039
TI  - Machine learning models to predict myocardial infarctions from past climatic and environmental conditions.
JO  - Hat. Hazards Earth Syst. Sci.
VL  - 22
IS  - 9
PY  - 2022
SN  - 1561-8633
ER  - 

TY  - JOUR
AB  - Dead fine fuel (e.g., litter) moisture content is an important parameter for both forest fire and ecological applications as it is related to ignitability, fire behavior and soil respiration. Real-time availability of this value would thus be a great benefit to fire risk management and prevention. However, the comprehensive literature review in this paper shows that there is no easy-to-use method for automated measurements available. This study investigates the applicability of four different sensor types (permittivity and electrical resistance measuring principles) for this measurement. Comparisons were made to manual gravimetric reference measurements carried out almost daily for one fire season and overall agreement was good (highly significant correlations with 0.792 < Combining double low line r < Combining double low line 0.947, p < 0.001). Standard deviations within sensor types were linearly correlated to daily sensor mean values; however, above a certain threshold they became irregular, which may be linked to exceedance of the working ranges. Thus, measurements with irregular standard deviations were considered unusable and relationships between gravimetric and automatic measurements of all individual sensors were compared only for useable periods. A large drift in these relationships became obvious from drought to drought period. This drift may be related to installation effects or settling and decomposition of the litter layer throughout the fire season. Because of the drift and the in situ calibration necessary, it cannot be recommended to use the methods presented here for monitoring purposes and thus operational hazard management. However, they may be interesting for scientific studies when some manual fuel moisture measurements are made anyway. Additionally, a number of potential methodological improvements are suggested.
AU  - Schunk, C.*
AU  - Ruth, B.
AU  - Leuchner, M.*
AU  - Wastl, C.*
AU  - Menzel, A.*
C1  - 47958
C2  - 39828
CY  - Gottingen
SP  - 403-415
TI  - Comparison of different methods for the in situ measurement of forest litter moisture content.
JO  - Hat. Hazards Earth Syst. Sci.
VL  - 16
IS  - 2
PB  - Copernicus Gesellschaft Mbh
PY  - 2016
SN  - 1561-8633
ER  -