Data analytics, statistics, and more
Costs for groundwater monitoring represent a significant, persistent, and growing burden for environmental remediation projects. This post examines spatial optimization of a groundwater monitoring well network using a geostatistic approach to identify new well locations or redundant locations such that the operational value of the monitoring network is maximized.
The Mann-Kendall test and Theil-Sen regression are typically used to detect temporal trend when analyzing environmental data. Both tests are affected by any value chosen to represent non-detects. Another method that is not affected by non-detects is Akritas-Theil-Sen nonparametric regression.
Conduct an evaluation of surface temperature change using temperature measurements that have been collected at Kremsmünster Abbey in Austria, which is considered to be one of the highest quality, longest running, instrumental temperature records in the world.
Landscapes contain complex spatial patterns in the distribution of resources that vary over time. This post examines the spatial analysis of landscapes using base R functions complemented by contributed packages for spatial pattern analysis and for quantifying landscape characteristics.
This post presents single-well iterative thinning, which is a method to determine an optimal sampling frequency for groundwater monitoring wells and monitoring constituents.
Use a stochastic partial differential equation (SPDE) approach with Integrated Nested Laplace Approximation (INLA) to model the spatio-temporal behavior of PM10 concentrations measured in the North-Italian region of Piemonte.
The performance of Bayesian inference using a stochastic partial differential equation (SPDE) approach with Integrated Nested Laplace Approximation (INLA) for predicting zinc concentrations in soil at unsampled locations is compared with those obtained using kriging.
Multivariate statistical methods provide a means of exploring complex data sets for patterns and relationships from which hypotheses can be generated and subsequently tested. This post explores methods to manage non-detects when applying multivariate procedures to investigate (dis)similarities among data objects based on a set of descriptors.
Develope an unstratified, equal probability sample design and a stratified, equal probability sample design using Generalized Random Tessellation Stratified (GRTS) sampling.
Time-series analysis and forecasting is an important area of machine learning because many predictive learning problems involve a time component. This post examines time-series analysis using indoor air concentrations of trichloroethene and various explanatory varibles collected over time at a single location.