A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

Wind multipliers are factors that transform wind speeds over open, flat terrain (regional wind speeds) to local wind speeds that consider the effects of direction, terrain (surface roughness), shielding (buildings and structures) and topography (hills and ridges). During the assessment of local wind hazards (spatial significance in the order 10's of metres), wind multipliers allow for regional wind speeds (order 10 to 100's of kilometres) to be factored to provide local wind speeds. <b>Value: </b>The wind multiplier data is used in modelling the impacts (i.e. physical damage) of wind-related events such as tropical cyclones (an input for Tropical Cyclone Risk assessment), thunderstorms and other windstorms. <b>Scope: </b>Includes terrain, shielding and topographic multipliers for national coverage. Each multiplier further contains 8 directions.

This dataset provides geospatial representation of the Australian wind regions defined in AS/NZS 1170.2 (2021) Structural Design Actions Part 2: wind actions (hereafter “Standard”). The dataset is intended to assist in delineating areas for referencing the Standard – for example in assigning building vulnerability models across the country. The dataset represents Geoscience Australia's interpretation of the definitions set out in the Standard and is intended for internal use only. This dataset is not suitable for design purposes: professional designers should refer to the Standard for assessing the wind region for their projects. In the event of any inconsistency between this dataset and Figure 3.1 in the Standard, the Standard will take precedence. This product has not been formally endorsed by Standards Australia or the relevant Working Groups and subcommittees. References to localities are indicative and use the best available information at the time of production. For further information on this dataset, please contact hazards@ga.gov.au.