MODELING NITRATE CONCENTRATION IN GROUND WATER USING REGRESSION AND NEURAL NETWORKS

Nitrate concentration in ground water is a major problem in specific agricultural areas. Using regression and neural networks, this study models nitrate concentration in ground water as a function of iron concentration in ground water, season and distance of the well from a poultry house. Results from both techniques are comparable and show that the distance of the well from a poultry house has a significant effect on nitrate concentration in groundwater.Environmental Economics and Policy, Livestock Production/Industries,

MODELING NITROGEN LOADING RATE TO DELAWARE LAKES USING REGRESSION AND NEURAL NETWORKS

The objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models.Environmental Economics and Policy,

Opto-Electronic Sensing of Soil Organic Matter

170 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1979.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

IN-PATIENT FLOW ANALYSIS USING PROMODEL (TM) SIMULATION PACKAGE

This paper emphasizes the basic modeling approach of general in-patient flow in a major hospital in the East Coast region. Simulation was used to analyze the inpatient flow. The first objective of this study was to determine the bottlenecks for in-in-patient flow. In order to understand the general in-patient flow, some emphasis was also given to the other units such as Medical-Surgical, Telemetry, Intensive Care Units (ICU), etc. Second objective was to study the impact of bed availability on the waiting time of admitted patients in ED before being transferred to assigned beds in other units of the hospital. A preliminary model was developed and validated based on the data collected for the selected time periods (busy four months). Different what-if scenarios were studied. This paper presents the basic model and its results. Key Words: simulation, in-patient flow, health sciencesimulation, in-patient flow, health science, Health Economics and Policy,

IN-PATIENT FLOW ANALYSIS USING PROMODEL (TM) SIMULATION PACKAGE

This paper emphasizes the basic modeling approach of general in-patient flow in a major hospital in the East Coast region. Simulation was used to analyze the inpatient flow. The first objective of this study was to determine the bottlenecks for in-in-patient flow. In order to understand the general in-patient flow, some emphasis was also given to the other units such as Medical-Surgical, Telemetry, Intensive Care Units (ICU), etc. Second objective was to study the impact of bed availability on the waiting time of admitted patients in ED before being transferred to assigned beds in other units of the hospital. A preliminary model was developed and validated based on the data collected for the selected time periods (busy four months). Different "what-if" scenarios were studied. This paper presents the basic model and its results. Key Words: simulation, in-patient flow, health scienc

MODELING NITRATE CONCENTRATION IN GROUND WATER USING REGRESSION AND NEURAL NETWORKS

Nitrate concentration in ground water is a major problem in specific agricultural areas. Using regression and neural networks, this study models nitrate concentration in ground water as a function of iron concentration in ground water, season and distance of the well from a poultry house. Results from both techniques are comparable and show that the distance of the well from a poultry house has a significant effect on nitrate concentration in groundwater

MODELING NITROGEN LOADING RATE TO DELAWARE LAKES USING REGRESSION AND NEURAL NETWORKS

The objective of this research was to predict the nitrogen-loading rate to Delaware lakes and streams using regression analysis and neural networks. Both models relate nitrogen-loading rate to cropland, soil type and presence of broiler production. Dummy variables were used to represent soil type and the presence of broiler production at a watershed. Data collected by Ritter & Harris (1984) was used in this research. To build the regression model Statistical Analysis System (SAS) was used. NeuroShell Easy Predictor, neural network software was used to develop the neural network model. Model adequacy was established by statistical techniques. A comparison of the regression and neural network models showed that both perform equally well. Cropland was the only significant variable that had any influence on the nitrogen-loading rate according to both the models