• Journal of Environmental Science International
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• v.16 no.12
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• pp.1319-1324
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• 2007

A tank model in conjunction with Kalman filter is developed for prediction of sediment yield from an upland watershed in Northwestern Mississippi. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error. The sediment yield of each tank is computed by multiplying the total sediment yield by the sediment yield coefficient. The sediment concentration of the first tank is computed from its storage and the sediment concentration distribution(SCD); the sediment concentration of the next lower tank is obtained by its storage and the sediment infiltration of the upper tank; and so on. The sediment yield computed by the tank model using Kalman filter was in good agreement with the observed sediment yield and was more accurate than the sediment yield computed by the tank model.

• Journal of Environmental Science International
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• v.18 no.1
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• pp.1-7
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• 2009

In this study a sediment yield is compared by IUSG, IUSG with Kalman filter, tank model and tank model with Kalman filter separately. The IUSG is the distribution of sediment from an instantaneous burst of rainfall producing one unit of runoff. The IUSG, defined as a product of the sediment concentration distribution (SCD) and the instantaneous unit hydrograph (IUH), is known to depend on the characteristics of the effective rainfall. In the IUSG with Kalman filter, the state vector of the watershed sediment yield system is constituted by the IUSG. The initial values of the state vector are assumed as the average of the IUSG values and the initial sediment yield estimated from the average IUSG. A tank model consisting of three tanks was developed for prediction of sediment yield. The sediment yield of each tank was computed by multiplying the total sediment yield by the sediment yield coefficients; the yield was obtained by the product of the runoff of each tank and the sediment concentration in the tank. A tank model with Kalman filter is developed for prediction of sediment yield. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• Structural Engineering and Mechanics
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• v.6 no.4
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• pp.395-409
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• 1998

This paper concerns the development and implementation of an orthotropic, stress resultant elasto-plastic finite element model for the collapse load analysis of reinforced concrete plates. The model implements yield line plasticity theory for reinforced concrete. The behaviour of the yield functions are studied, and modifications introduced to ensure a robust finite element model of cases involving bending and twisting stress resultants ($M_x$, $M_y$, $M_{xy}$). Onset of plasticity is always governed by the general yield-line-model (YLM), but in some cases a switch to the stress resultant form of the von Mises function is used to ensure the proper evolution of plastic strains. Case studies are presented, involving isotropic and orthotropic plates, to assess the behaviour of the yield line approach. The YLM function is shown to perform extremely well, in predicting both the collapse loads and failure mechanisms.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.7
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• pp.915-921
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• 2006

Various random regression models with different order of Legendre polynomials for permanent environmental and genetic effects were constructed to predict future milk yield of Holstein cows in Korea. A total of 257,908 test-day (TD) milk yield records from a total of 28,135 cows belonging to 1,090 herds were considered for estimating (co)variance of the random covariate coefficients using an expectation-maximization REML algorithm in an animal mixed model. The variances did not change much between the models, having different order of Legendre polynomial, but a decreasing trend was observed with increase in the order of Legendre polynomial in the model. The R-squared value of the model increased and the residual variance reduced with the increase in order of Legendre polynomial in the model. Therefore, a model with $5^{th}$ order of Legendre polynomial was considered for predicting future milk yield. For predicting the future milk yield of cows, 132,771 TD records from 28,135 cows were randomly selected from the above data by way of preceding partial TD record, and then future milk yields were estimated using incomplete records from each cow randomly retained. Results suggested that we could predict the next four months milk yield with an error deviation of 4 kg. The correlation of more than 70% between predicted and observed values was estimated for the next four months milk yield. Even using only 3 TD records of some cows, the average milk yield of Korean Holstein cows would be predicted with high accuracy if compared with observed milk yield. Persistency of each cow was estimated which might be useful for selecting the cows with higher persistency. The results of the present study suggested the use of a $5^{th}$ order Legendre polynomial to predict the future milk yield of each cow.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• Asian-Australasian Journal of Animal Sciences
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• v.8 no.4
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• pp.365-368
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• 1995

Lactation curves of dairy cows were generated using three models, namely; incomplete gamma function (model 1), polynomial inverse function (model 2) and non-linear regression (model 3). Secondary milk yield data of 27 cows which had completed 6 lactations were used in this study. Milk yield records (once a week) throughout the lactation and from the first three months of lactation were fitted to the models. Estimation of total milk yield by model 3 using the data once a week throughout the lactation resulted in smaller % bias and standard error than those generated from model 1 and 2. But, model 2 was more accurate in predicting the 305-day milk yield equivalent closer to actual yields with smaller bias % and error using partial records up to 3 months. Also, model 2 was able to estimate the time to reach peak yield close to the actual data using partial records and model 2 could be used as a tool to advise farmers on appropriate feeding and management practices to be adopted.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.21-31
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• 2015

Various crop models have been extensively used for estimation of the crop yields. Compared to the other models, the EPIC model uses a unified approach to simulate more than 100 types of crops. It has been successfully applied in simulating crop yields for various combinations of weather conditions, soil properties, crops, and management schemes in many countries. The objective of this study was to estimate the rice and maize yield in South Korea using the EPIC model. The input datasets for the 30 types in the 11 categories were created for the EPIC model. The EPIC model simulated rice and maize yields. The performance of the EPIC model was evaluated with the goodness-of-fit measures including Root Mean Square Error (RMSE), Relative Error (RE), Nash-Sutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE), and Pearson Correelation Coefficient (r). The rice yield showed to more high accuracy than maize yield on four type of method without NSEC. Theses results showed that the EPIC model better simulated rice yields than maize yields. The results suggest that the EPIC crop model can be useful to estimate crop yield in South Korea.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06b
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• pp.305-309
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• 2006

This paper presents and illustrates the application of a growth and yield model that supports both forest and mill side volume and value estimates. Traditional forest stand growth and yield models represent the forest landowner view of yield and economics. Predicted yields are estimates of what one would expect from a procurement cruise, and current stumpage prices are applied to investigate optimum management strategies. Optimum management regimes and rotation ages obtained from the forest side view are unlikely to be economically optimal when viewed from the mill side. The actual distribution of recoverable manufactured product and its value are highly dependent on mill technologies and configurations. Overcoming this limitation of growth and yield computer models necessitates the ability to predict and price the expected manufactured distribution of lumber, lineal meters of veneer, and tonnes of air dried pulp fiber yield. With these embedded models, users of the yield simulator can evaluate the economics of possible/feasible management regimes from both the forest and mill business sides. The simulator is a forest side model that has been modified to produce estimates of manufactured product yields by embedding models for 1) pulpwood chip size class distribution and pulp yield for any kappa number (Schultz and Matney, 2002), 2) a lumber yield and pricing model based on the Best Opening Face model developed by the USDA Forest Service Forest Products Laboratory (Lewis, 1985a and Lewis, 1985b), and 3) a lineal meter veneer model derived from peeler block tests. While the model is strictly applicable to planted loblolly pine (Pinus taeda L.) on cutover site-prepared land in the United States (US) Gulf South, the model and computer program are adaptable to any region and forest type.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.2
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• pp.174-179
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• 2010

In order to interpret yield response of sugar beet to nitrogen fertilizer, and pig manure compost in saline-sodic soil of reclaimed tidal land, 4 kinds of response model, i.e., quadratic, exponential, square root, and linear response, and plateau model, are applied. The root fresh yield of sugar beet decreased exponentially with the increase of soil EC. The root fresh yield of sugar beet to nitrogen fertilizer was fitted best to the linear response, and plateau model among 4 yield response models with highly significant determination coefficient ($R^2=0.92^{**}$). The optimum N rate determined on the model was 138 kg N $ha^{-1}$. The root fresh yield of sugar beet to pig manure compost was fitted best to the quadratic model among 4 yield response models with highly significant determination coefficient ($R^2=0.99^{**}$). The maximum N rate determined on the model was 9.17 ton $ha^{-1}$. In conclusion, the proper model to interpret the yield of sugar beet in saline-sodic soil differs with the kinds of nutrient, linear response, and plateau model for fertilizer nitrogen, and quadratic model to pig manure compost.