Application of combined model with DGM(1,1) and linear regression in grain yield prediction

Purpose The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values. Findings The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction. Practical implications The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

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Oat yield through panicle components and growth regulator

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v21n4p261-266 ◽

2017 ◽

Vol 21 (4) ◽

pp. 261-266

Author(s):

Anderson Marolli ◽

José A. G. da Silva ◽

Rubia D. Mantai ◽

Ana P. Brezolin ◽

Maria E. Gzergorczick ◽

...

Keyword(s):

Linear Regression ◽

Regression Model ◽

Grain Yield ◽

Multiple Linear Regression ◽

Growth Regulator ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

N Fertilizer ◽

Regression Equations ◽

Very High

ABSTRACT The growth regulator modifies the expression of lodging and panicle components in oat plants, with reflexes in yield. The objective of this study was to define the optimal dose of growth regulator in oat for a maximum lodging of 5%. In addition, this study aimed to identify potential variables of the panicle to compose the multiple linear regression model and the simulation of grain yield in conditions of use of the regulator under low, high and very high fertilization with nitrogen. The study was conducted in 2011, 2012 and 2013 in a randomized block design with four replicates in a 4 x 3 factorial scheme, for growth regulator doses (0, 200, 400 and 600 mL ha-1) and N-fertilizer doses (30, 90 and 150 kg ha-1), respectively. The growth regulator doses of 395, 450 and 560 mL ha-1 are efficient, with maximum oat lodging of 5%, under low, high and very high nitrogen fertilization, respectively. The grain weight per panicle and panicle harvest index are potential variables to compose the multiple linear regression model. Multiple linear regression equations are efficient in the simulation of oat grain yield under the conditions of use of growth regulator, regardless of the N-fertilizer dose.

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An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191218124350 ◽

2020 ◽

Vol 10 (4) ◽

pp. 473-483

Author(s):

Pundra Chandra Shaker Reddy ◽

Alladi Sureshbabu

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Weather Prediction ◽

Big Data Analytics ◽

Multiple Linear Regression Model ◽

Seasonal Rainfall ◽

Climate Data ◽

Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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A Multiple Linear Regression Model for Maintainability Analysis Considering Maintainability Design Characteristics

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3424988 ◽

2020 ◽

Author(s):

Zhenya Wu ◽

Jianping Hao ◽

Peng Wang ◽

Chenguang Liu ◽

Zhen Li

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Design Characteristics ◽

Maintainability Analysis

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Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

Clinical Neuroradiology ◽

10.1007/s00062-021-01067-5 ◽

2021 ◽

Author(s):

Olivia Fösleitner ◽

Véronique Schwehr ◽

Tim Godel ◽

Fabian Preisner ◽

Philipp Bäumer ◽

...

Keyword(s):

Skeletal Muscle ◽

Body Weight ◽

Sciatic Nerve ◽

Linear Regression ◽

Peripheral Nerve ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Magnetization Transfer ◽

Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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