A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA.

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area.

Predicting Arsenic in Drinking Water Wells of the Central Valley, California.

Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-learner ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant.

Measurements of the [Formula: see text][Formula: see text] production cross sections in the [Formula: see text] channel in proton-proton collisions at [Formula: see text] and [Formula: see text] and combined constraints on triple gauge couplings.

Measurements of the [Formula: see text][Formula: see text] production cross sections in proton-proton collisions at center-of-mass energies of 7 and 8[Formula: see text] are presented. Candidate events for the leptonic decay mode [Formula: see text], where [Formula: see text] denotes an electron or a muon, are reconstructed and selected from data corresponding to an integrated luminosity of 5.1 (19.