Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

An ensemble of neural networks for weather forecasting

Abstract: This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.

In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index

Abstract: Drastic increases in the cost of N fertilizer and increased public scrutiny have encouraged development and implementation of improved N management practices. This study evaluated the relationship between corn (Zea mays L.) grain yield and early season normalized difference vegetation index (NDVI) sensor readings using the GreenSeeker sensor. The relationships between grain yield and several predictor variables were determined using linear and nonlinear regression analysis. Categorizing NDVI measurement by leaf stage indicated that growth stagewas criticalfor predictinggrain yield potential. Poor exponential relationships existed between NDVI from early sensor measurements (V6–V7 leaf stage) and grain yield. By the V8 stage, a strong relationship (R 2 5 0.77) was achieved between NDVI and grain yield. Later sensor measurements (V9 and later) failed to distinguish variation in green biomass as a result of canopy closure. Normalizing the NDVI with GDD (growing degree days) did not significantly improve yield potential prediction (R 2 5 0.73), but broadened the yield potential prediction equation to include temperature and allowed for adaptation into various climates. Sensor measurements at the range of 800 to 1000 GDD resulted in a significant exponential relationship between grain yield and NDVI (R 2 5 0.76) similar to the V8 leaf stage categorization. Categorizing NDVI by GDD (800–1000 GDD) extended the sensing time by two additional leaf stages (V7–V9) to allow a practical window of opportunity for sidedress N applications. This study showed that yield potential in corn could be accurately predicted in season with NDVI measured with the GreenSeeker sensor.

Reliability‐Based Optimization Model for Water Distribution Systems

Abstract: This paper presents the basic framework for a model that can be used to determine the optimal (least‐cost) design of a water distribution system subject to continuity, conservation of energy, nodal head bounds, and reliability constraints. Reliability is defined as the probability of satisfying nodal demands and pressure heads for various possible pipe failures (breaks) in the water distribution system. The overall model includes three that are linked: a steady‐state simulation model, a reliability model, and an optimization model. The simulation model is used to implicitly solve the continuity and energy constraints and is used in the reliability model to define minimum cut sets. The reliability model, which is based on a minimum cut‐set method, determines the values of system and nodal reliability. The optimization model is based on a generalized reduced‐gradient method. Examples are used to illustrate the model.

Use of Diammonium Phosphate to Reduce Heavy Metal Solubility and Transport in Smelter-Contaminated Soil

Abstract: Phosphate treatments can reduce metal dissolution and transport from contaminated soils. However, diammonium phosphate (DAP) has not been extensively tested as a chemical immobilization treatment. This study was conducted to evaluate DAP as a chemical immobilization treatment and to investigate potential solids controlling metal solubility in DAP-amended soils. Soil contaminated with Cd, Pb, Zn, and As was collected from a former smelter site. The DAP treatments of 460, 920, and 2300 mg P kg -1 and an untreated check were evaluated using solute transport experiments. Increasing DAP decreased total metal transported. Application of 2300 mg P kg -1 was the most effective for immobilizing Cd, Pb, and Zn eluted from the contaminated soil. Metal elution curves fitted with a transport model showed that DAP treatment increased retardation (R) 2-fold for Cd, 6-fold for Zn, and 3.5-fold for Pb. Distribution coefficients (K d ) increased with P application from 4.0 to 9.0 L kg -1 for Cd, from 2.9 to 10.8 L kg -1 for Pb, and from 2.5 to 17.1 L kg -1 for Zn. Increased K d values with additional DAP treatment indicated reduced partitioning of sorbed and/or precipitated metal released to mobile metal phases and a concomitant decrease in the concentration of mobile heavy metal species. Activity-ratio diagrams indicated that DAP decreased solution Cd, Pb, and Zn by forming metal-phosphate precipitates with low solubility products. These results suggest that DAP may have potential for protecting water resources from heavy metal contamination near smelting and mining sites.

Premarital education, marital quality, and marital stability: findings from a large, random household survey.

Abstract: One of the limitations of experimental studies on the effectiveness of premarital education is the reliance on samples of mostly White, middle-class couples. In contrast, although survey methods allow only weak inferences about causal relations, representative surveys can yield important information about use and estimated effects across a diverse population. Using a large random survey of 4 middle American states, the authors found that participation in premarital education was associated with higher levels of satisfaction and commitment in marriage and lower levels of conflict-and also reduced odds of divorce. These estimated effects were robust across race, income (including among the poor), and education levels, which suggests that participation in premarital education is generally beneficial for a wide range of couples.