An alternative delayed population growth difference equation model

The Beverton and Holt and Ricker stock–recruitment curves can be used to generate population growth curves. The Beverton and Holt curve is then identical to a difference equation model for the logistic growth curve, and may be derived in terms of equations for linearly density-dependent population regulation. The same equations lead to the Ricker curve if the density-regulating effect is assumed to depend only on population size at the beginning of the interval between generations. At low rates of population growth, the Ricker curve approaches that of Beverton and Holt. The two curves appear to represent certain concepts known in population biology as "r and K selection."

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A difference equation model of partial series

Applied Mathematics and Computation ◽

10.1016/0096-3003(94)90052-3 ◽

1994 ◽

Vol 61 (2-3) ◽

pp. 301-305

Author(s):

K.J. Bunch ◽

R.W. Grow

Keyword(s):

Difference Equation ◽

Equation Model

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Lagrangian Integro-Difference Equation Model for Precipitation Nowcasting

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-21-0013.1 ◽

2021 ◽

Author(s):

Seppo Pulkkinen ◽

V. Chandrasekar ◽

Tero Niemi

Keyword(s):

Difference Equation ◽

Urban Areas ◽

Short Range ◽

Autoregressive Process ◽

Skill Score ◽

Equation Model ◽

Validation Dataset ◽

Time Range ◽

Stochastic Perturbations ◽

Second Best

AbstractDelivering reliable nowcasts (short-range forecasts) of severe rainfall and the resulting flash floods is important in densely populated urban areas. The conventional method is advection-based extrapolation of radar echoes. However, during rapidly evolving convective rainfall this so-called Lagrangian persistence (LP) approach is limited to deterministic and very short-range nowcasts. To address these limitations in the one-hour time range, a novel extension of LP, called Lagrangian INtegro-Difference equation model withAutoregression (LINDA), is proposed. The model consists of five components: 1) identification of rain cells, 2) advection, 3) autoregressive process describing growth and decay of the cells, 4) convolution describing loss of predictability at small scales and 5) stochastic perturbations to simulate forecast uncertainty. Advection is separated from the other components that are applied in the Lagrangian coordinates. The reliability of LINDA is evaluated using the NEXRAD WSR-88D radar that covers the Dallas-Fort Worth metropolitan area, as well as the NEXRAD mosaic covering the continental United States. This is done with two different configurations: LINDA-D for deterministic and LINDA-P for probabilistic nowcasts. The validation dataset consists of 11 rainfall events during 2018-2020. For predicting moderate to heavy rainfall (5-20 mm/h), LINDA outperforms the previously proposed LP-based approaches. The most significant improvement is seen for the ETS and POD statistics with the 5 mm/h threshold. For 30-minute nowcasts, they show 15% and 16% increase, respectively, to the second-best method and 48% and 34% increase compared to LP. For the 5 mm/h threshold, the increase in the ROC skill score of 30-minute nowcasts from the second-best method is 10%.

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Development of a Tree Growth Difference Equation and Its Application in Forecasting the Biomass Carbon Stocks of Chinese Forests in 2050

Forests ◽

10.3390/f10070582 ◽

2019 ◽

Vol 10 (7) ◽

pp. 582 ◽

Cited By ~ 3

Author(s):

Hanyue Zhang ◽

Zhongke Feng ◽

Panpan Chen ◽

Xiaofeng Chen

Keyword(s):

Climate Change ◽

Difference Equation ◽

Tree Growth ◽

Difference Equations ◽

Land Surface ◽

Carbon Stocks ◽

Sufficient Information ◽

Biomass Carbon ◽

Growth Difference ◽

The Relationship

Global climate change has raised concerns about the relationship between ecosystems and forests, which is a core component of the carbon cycle and a critical factor in understanding and mitigating the effects of climate change. Forest models and sufficient information for predictions are important for ensuring efficient afforestation activities and sustainable forest development. Based on the theory of difference equations and the general rules of tree growth, this study established a difference equation for the relationship between the ratio of tree diameter at breast height (DBH) to the tree height and age of age of China’s main arbor species. A comparison with equations that represent the traditional tree growth models, i.e., Logistic and Richards equations, showed that the difference equations exhibited higher precision for both fitting and verification data. Moreover, the biomass carbon stocks (BCS) of Chinese forests from 2013 to 2050 were predicted by combining the 8th Chinese Ministry of Forestry and partial continuous forest inventory (CFI) data sets. The results showed that the BCS of Chinese forests would increase from 7342 to 11,030 terra grams of carbon (Tg C) in 2013–2050, with an annual biomass C (carbon) sink of 99.68 Tg C year−1, and they indicated that the Chinese land-surface forest vegetation has an important carbon sequestration capability.

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On the Complete "Discretization" of an nth -Order Linear Differential Equation Model to Obtain the Exact nth-Order Difference Equation Model with Correct "Initial-Sequence Values"

Mathematical and Computer Modelling of Dynamical Systems ◽

10.1076/mcmd.5.1.74.3622 ◽

1999 ◽

Vol 5 (1) ◽

pp. 74-84

Author(s):

C.D. Johnson

Keyword(s):

Differential Equation ◽

Difference Equation ◽

Linear Differential Equation ◽

Equation Model ◽

Order Linear Differential Equation ◽

Differential Equation Model ◽

Order Difference ◽

Initial Sequence ◽

Order Difference Equation ◽

Linear Differential

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Factors Impinge on Development of a Smart City: A Field Study

10.21203/rs.3.rs-589772/v1 ◽

2021 ◽

Author(s):

Priya Jothimani ◽

Palanisamy Chenniappan ◽

Vinothini Chidambaranathan

Keyword(s):

Population Growth ◽

Smart City ◽

Structural Equation ◽

Smart Cities ◽

Descriptive Analysis ◽

Equation Model ◽

Urban Life ◽

Human Environment ◽

Significant Area ◽

Chennai City

Abstract Smart City aims at amassed connectivity at various levels in the midst of citizens, as well as amid the administration and the daily growing population. India is one of the developing country where population growth is one of the significant area is to be noted seriously. A city is a large and permanent human environment that provides its citizens with many services and opportunities. The rapid economic growth and population growth have put a huge amount of strain on urban infrastructure and service provision. India is an under developing nation to modernize urban life, the current urbanization needs good tactics and creative planning. India’s government has launched 100 Smart Cities where it is expected that citizens will use new innovations and resolve the issues. Smart Cities are intended for finest usage of space and resources along with an effectual and optimum dissemination of benefits. This study is to investigate and analysis of Chennai Smart city Mission (SCM) development. This work has been undertaken to learn about the aspects of Smart Development and the factors that governing Smart City. The analysis has been split up into 4 portions as Questionaries’ survey in the Chennai city, Frequency and Percentage analysis, Descriptive analysis and using Structural equation modelling (SCM). Using the SPSS (Statistical Package for Social Sciences) version 21.0, conversational interviewing, and questionnaire survey and also journal study are conducted to find factors influencing the implementation of smart city and reviewed. Using the SEM (structural equation model) AMOS 21.0 software, confirmatory factor assessment had done. This study gives in-depth knowledge in implementation of the smart city scheme aspects and also suggests solution for most affecting factor in a city.

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