Sampling Plan Optimization: A Data Review and Sampling Frequency Evaluation Process

Abstract

Lawrence Livermore National Laboratory (LLNL) uses a cost‐effective sampling (CES) methodology to evaluate and review ground water contaminant data and optimize the site's ground water monitoring plan. The CES methodology is part of LLNL's regulatory approved compliance monitoring plan (Lamarre et al. 1996). It allows LLNL to adjust the ground water sampling plan every quarter in response to changing conditions at the site. Since the use of the CES methodology has been approved by the appropriate regulatory agencies, such adjustments do not need additional regulatory approval. This permits LLNL to respond more quickly to changing conditions. The CES methodology bases the sampling frequency for each location on trend, variability, and magnitude statistics describing the contaminants at that location, and on the input of the technical staff (hydrologists, chemists, statisticians, and project leaders). After initial setup is complete, each application of CES takes only a few days for as many as 400 wells. Effective use of the CES methodology requires sufficient data, an understanding of contaminant transport at the site, and an adequate number of monitoring wells downgradient of the contamination. The initial implementation of CES at LLNL in 1992 produced a 40% reduction in the required number of annual routine ground water samples at LLNL. This has saved LLNL $390,000 annually in sampling, analysis, and data management costs.

Environmental sound classification using convolution neural networks with different integrated loss functions

“…In order to convert the audio into meaningful spectrograms, we have to follow some preliminary steps, which include deciding the sample rate so that audio is converted into discrete values of amplitude in a long NumPy array. Choosing a large sample rate would generate a long list of numbers that can be put together to construct the original sound again (Ridley & MacQueen, 2004). However, a small sample rate would result in the loss of valuable features from the audio whereas we used a default sample rate of from LibROSA (McFee et al, 2015), which is a python package explicitly built for audio analysis.…”

Reducing Spatial Sampling in Long-Term Groundwater Monitoring Networks Using Ant Colony Optimization

“…Reference [19] developed the cost-effective sampling (CES) method to reduce sampling frequency through a decision support system based on trend analysis and simple statistical measures. However, MAROS, GTS, and CES are not mathematical optimization methods; they are decision support tools in which manual iterative steps rather than automated processes are used to improve existing LTM networks in a sequential ranking procedure.…”