This paper proposes a 2D dynamical model for evaluating parameters affecting whiplash. In fact a four segment dynamical model is developed in the sagittal plane for the analysis. The model response is validated using the existing experimental data and is shown to simulate the "S-Shape" and "initial upward ramping" kinematics of the cervical spine and the resulting dynamics observed in human and...

To overcome the difficulty that existing hydrological models cannot accurately simulate processes with limited information in irrigated paddy areas southern China, this paper presents a prediction model combining Ensemble Empirical Mode Decomposition (EEMD) method and Long Short-Term Memory (LSTM) network. Meteorological factors were set as multivariate input to model. Rainfall, regarded main v...

land surface parameterization schemes are one of the most important components in gcm and nwp models. these schemes calculate the exchanges of momentum, mass and energy between land surface and the atmosphere. runoff is one of the important components in the water cycle of the land surface scheme whose parameterization is difficult because of complexity in the processes governing the runoff gen...

introduction: snowmelt runoff plays an important role in providing water and agricultural resources, especially in mountainous areas. there are different methods to simulate the process of snowmelt. inter alia, degree-day model, based on temperature-index is more cited. snowmelt runoff model is a conceptual hydrological model to simulate and predict the daily flow of rivers in the mountainous b...

Neural networks may provide a user-friendly alternative or supplement to complex physically based models for soil erosion prediction for some study areas. The purpose of this study was to investigate the applicability of using neural networks to quantitatively predict soil loss from natural runoff plots. Data from 2879 erosion events from eight locations in the United States were used. Neural n...

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. ...

Quantifying effectiveness of agricultural BMPs at the watershed scale is a challenging issue, requiring robust algorithms to simulate not only the agricultural production system but also pollutant transport and fate. This research addresses the challenge to simulate performances of BMPs in reducing organophosphates (OPs) runoff at the watershed scale. The SWAT model is calibrated and validated ...

Flood is one of the most calamitous natural disasters that causes extensive property and life damages across theworld. It however, could be a blessing due to its special natural water resources recharging value. By simulating themagnitude of probable floods considering the anthropogenic and natural effects and implementing contingency plans,their damages could be reduced. In this paper, the Gen...