Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed.

Prediction and quantification of nutrient concentrations in surface water has gained substantial attention during recent decades because excess nutrients released from agricultural and urban watersheds can significantly deteriorate surface water quality. Machine learning (ML) models are considered an effective tool for better understanding and characterization of nutrient release from agricultural fields to surface water. However, to date, no systematic investigations have examined the implementation of different classification and regression ML models in agricultural settings to predict nutrient concentrations in surface water using a group of input variables including climatological (e.

Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed.

Excess nutrients in surface water and groundwater can lead to water quality deterioration in available water resources. Thus, the classification of nutrient concentrations in water resources has gained significant attention during recent decades. Machine learning (ML) algorithms are considered an efficient tool to describe nutrient loss from agricultural land to surface water and groundwater.

Test-Retest Variability and Discriminatory Power of Measurements From Microperimetry and Dark Adaptation Assessment in People With Intermediate Age-Related Macular Degeneration - A MACUSTAR Study Report.

Purpose: The purpose of this study was to assess test-retest variability and discriminatory power of measures from macular integrity assessment (S-MAIA) and AdaptDx.

Methods: This is a cross-sectional study of 167 people with intermediate age-related macular degeneration (iAMD), no AMD (controls; n = 54), early AMD (n = 28), and late AMD (n = 41), recruited across 18 European ophthalmology centers. Repeat measures of mesopic and scotopic S-MAIA average (mean) threshold (MMAT decibels [dB] and SMAT [dB]) and rod intercept time (RIT [mins]) at 2 visits 14 (±7) days apart were recorded.

Investigating relationships between climate controls and nutrient flux in surface waters, sediments, and subsurface pathways in an agricultural clay catchment of the Great Lakes Basin.

Water quality within agricultural catchments is governed by management practices and climate conditions that control the transport, storage, and exchange of nutrients between components of the hydrologic cycle. This study aims to improve knowledge of nitrogen (N) and phosphorus (P) transport in low permeability agricultural watersheds by considering spatial and temporal trends of surface water nutrient concentrations in relation to hydroclimatic drivers, sediment quality, shallow hyporheic exchange, groundwater quality, and tile drain discharge over a 14-month field study in a clay hydrosystem of the Lake Huron basin, one of the five Great Lakes. Results found that events of varying magnitude and intensity enhanced nutrient release from overland flow and subsurface pathways.