Software
The Lower Mississippi-Gulf Water Science Center provides software resources for data retrieval and form submission. Below are resources made available to our cooperators as well as the public.
Filter Total Items: 23
mmlCHICOTla, Source code for construction and implementation of multiple machine-learning models of water levels in the Chicot aquifer system, southwestern Louisiana mmlCHICOTla, Source code for construction and implementation of multiple machine-learning models of water levels in the Chicot aquifer system, southwestern Louisiana
The mmlCHICOTla repository contains R, Mermaid, and Perl language source code that can be used for construction multiple methods of machine learning (MML) of water levels in the Chicot aquifer system, southwestern Louisiana. The source code is written in R (primary and extensive), Mermaid (workflow diagrams), and Perl (minor utility for simple implementation of parallel processing on...
covCHICOTla, Source code for construction of covariates bound to monthly groundwater levels for statistical modeling of water levels in the Chicot aquifer system, southwestern Louisiana covCHICOTla, Source code for construction of covariates bound to monthly groundwater levels for statistical modeling of water levels in the Chicot aquifer system, southwestern Louisiana
The covCHICOTla repository contains primarily R and limited Mermaid, Perl, and Python language source code that collectively can be used for the construction of many tables of monthly groundwater levels (response variable) and predictor variables (covariates) intended to be inputs to statistical modeling of water levels in the Chicot aquifer system, southwestern Louisiana. The source...
Research software technical note and source code for creation of statistical models for prediction of typical well depths and related spatial diagnostics for the Chicot aquifer system, southwestern Louisiana Research software technical note and source code for creation of statistical models for prediction of typical well depths and related spatial diagnostics for the Chicot aquifer system, southwestern Louisiana
This "software technical note" (`TECHNOTE.md`) provides extensive details involving the construction of typical well-depth grids from research-grade statistical study of the Chicot aquifer system. The README also within this directory is designed and intended to parallel the documentation of individual covariate construction as part of the primal feature of the **covCHICOTla** software...
mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer mmlMRVAgen1, Source Code for Construction of Multiple Machine-Learning Models of Water Levels in the Mississippi River Valley Alluvial Aquifer
The mmlMRVAgen1 repository contains R, LaTeX, Mermaid, and Perl language source code that can be used for construction multiple methods of machine learning (MML) of water levels in the Mississippi River Valley alluvial aquifer (MRVA) within the Mississippi Alluvial Plain (MAP), south-central United States. The source code is written in R (primary and extensive), LaTeX (for structured...
syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States
The syntheticdv2lff repository contains R language source code that document the workflow to compute low-streamflow (low-flow) frequency (LFF) statistics from previously estimated streamflow at level-12 hydrologic unit code (HUC12) pour points (watershed outlet locations) in the south-central and southeastern United States for 1950 through 2010 and associated bias correction. LFF...
syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States syntheticdv2lff, Scripts for low-flow frequency (LFF) estimation (and bias correction) from daily mean streamflow estimated at level-12 hydrologic unit code (HUC12) pour points in the southeastern United States
The syntheticdv2lff repository contains R language source code that document the workflow to compute low-streamflow (low-flow) frequency (LFF) statistics from previously estimated streamflow at level-12 hydrologic unit code (HUC12) pour points (watershed outlet locations) in the south-central and southeastern United States for 1950 through 2010 and associated bias correction. LFF...
infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities infoGWauxs, Auxiliary methods for infoGW and similar groundwater level data objects and other helpful utilities
The infoGWauxs package in the R language provides auxiliary methods for data merging, duplicate-record removal, and monthly rollup of groundwater levels in the so-called infoGW or GWmaster data objects. These data structures are particularly well suited for statistical analyses of groundwater levels. This package is part of a greater body of data processors oriented around the infoGW...
Aquaculture and Irrigation Water Use Model 2.0 Software Aquaculture and Irrigation Water Use Model 2.0 Software
The Mississippi Alluvial Plain (MAP) is one of the most productive agricultural regions in the US and extracts more than 11 km3/year for irrigation activities. The heavy drivers of groundwater use are aquaculture and crops, which include rice, cotton, corn, and soybeans (Wilson, 2021). Consequently, groundwater-level declines in the MAP region (Clark and others, 2011) pose a substantial...
RESTORE/TCEQswqmisESTUSAL, Source code for manipulation of data stemming from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program with emphasis on salinity change statistics for Texas coastal segments RESTORE/TCEQswqmisESTUSAL, Source code for manipulation of data stemming from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program with emphasis on salinity change statistics for Texas coastal segments
The RESTORE/TCEQswqmisESTUSAL repository contains R language source code that can be used for digesting data retrieved from the Texas Commission on Environmental Quality Surface Water Quality Monitoring Program through their Surface Water Quality Data Viewer. The workflow described concerns data manipulations, base data filtering, and computations towards documenting long-term salinity...
Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data Simulation and comparison of five estimators of variability in units of standard deviation for small samples drawn from normally distributed data
It is convenient to measure or estimate variation in samples or distributions in units of standard deviations. There are alternative methods of estimation of standard deviation aside from the conventional and well-known definition. Estimation of standard deviation for very small samples (as small as two), whereas not always ideal, might be useful in certain practical circumstances. A...
RESTORE/makESTUSAL, Source code for construction of various statistical models and prediction of daily salinity in coastal regions of the Gulf of Mexico, United States RESTORE/makESTUSAL, Source code for construction of various statistical models and prediction of daily salinity in coastal regions of the Gulf of Mexico, United States
The RESTORE/makESTUSAL software repository contains R language source code that can be used for the construction of various statistical models and output time series of predicted daily salinity coastal regions of the Gulf of Mexico, United States. The source code is expansive, and the repository is organizationally deep following logical organization units. One major subsystem of the...
Study of L-kurtosis and several distribution families for prediction of uncertainty distributions, An applied software technical note concerning L-kurtosis use in daily salinity prediction from multiple machine learning methods Study of L-kurtosis and several distribution families for prediction of uncertainty distributions, An applied software technical note concerning L-kurtosis use in daily salinity prediction from multiple machine learning methods
Statistical predictions that are based on multiple machine learning (MML) methods (from including differing training regimes) produce differing predictions. When the predictions are combined to a final estimate, then there are residuals of the predictions spread around the final estimate. It is common to assume normality or near-normality of the residuals (errors), but the assumption of...