Non-Linear and Nonparametric Modelling of Seasonal Environmental Data

2003 ◽  
Vol 18 (2) ◽  
pp. 167-183 ◽  
Author(s):  
A. McMullan ◽  
A. W. Bowman ◽  
E. M. Scott
Keyword(s):  
Environmental Data ◽  
Non Linear ◽  
Nonparametric Modelling

scholarly journals Non-Linear Analysis of River System Dynamics Using Recurrence Quantification Analysis

AppliedMath ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 1-15
Author(s):  
Athanasios Fragkou ◽  
Avraam Charakopoulos ◽  
Theodoros Karakasidis ◽  
Antonios Liakopoulos
Keyword(s):  
Time Series ◽  
Energy Policy ◽  
Linear Time ◽  
Time Windows ◽  
River System ◽  
Recurrence Quantification Analysis ◽  
Environmental Data ◽  
Recurrence Quantification ◽  
Non Linear ◽  
Quantification Analysis

Understanding the underlying processes and extracting detailed characteristics of rivers is critical and has not yet been fully developed. The purpose of this study was to examine the performance of non-linear time series methods on environmental data. Specifically, we performed an analysis of water level measurements, extracted from sensors, located on specified stations along the Nestos River (Greece), with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA) methods. A more detailed inspection with the sliding windows (epoqs) method was applied on the Recurrence Rate, Average Diagonal Line and Trapping Time parameters, with results showing phase transitions providing useful information about the dynamics of the system. The suggested method seems to be promising for the detection of the dynamical transitions that can characterize distinct time windows of the time series and reveals information about the changes in state within the whole time series. The results will be useful for designing the energy policy investments of producers and also will be helpful for dam management assessment as well as government energy policy.

The Effects of Modelling Techniques and Data Uncertainty in Wellhead Fatigue Life Calculation

2012 ◽  
Author(s):  
Dara Williams ◽  
John Greene
Keyword(s):  
Finite Element ◽  
Fatigue Life ◽  
Fatigue Damage ◽  
Environmental Data ◽  
Nonlinear Load ◽  
Soil Response ◽  
Load Response ◽  
Highly Nonlinear ◽  
Non Linear ◽  
The Impact

Offshore oil and gas exploration continues to move into deeper and more harsh environments and consequently the response of drilling riser systems and associated fatigue loading transmitted to the wellhead and conductor system are of key importance in the design of offshore wells. In addition the presence of ageing infrastructure in mature areas combined with requirements for future workover operations requires careful consideration of both past and future fatigue damage accumulation. In order to estimate remaining fatigue life for the wellhead and conductor the accumulation of damage from each stage of a drilling campaign and phase of operation of a well, including workover and completion operations, must be considered. Thus a detailed global finite element analysis of the impact of riser response, under wave and vortex induced vibration (VIV), on the conductor and wellhead structure is of critical importance. Traditional engineering evaluation methods to estimate fatigue of wellhead systems in offshore regions with limited availability of environmental data may result in an over estimation of fatigue damage accumulated in the wellhead. Any assumptions regarding fatigue current profiles can also lead to over-prediction of fatigue damage in the wellhead. This can have implications for the planning of future workover operations and may also lead to unnecessary over-design of the system. A further limitation of traditional wellhead fatigue evaluation criteria lies in the assumptions regarding riser tensioner system load response. These methods do not account for the highly nonlinear load response of the tensioner system and can thus significantly underestimate fatigue damage contribution. This paper presents a more detailed wellhead fatigue analysis methodology to incorporate new analysis techniques, as used for a number of recent applications, to assess with a greater level of refinement the impact of the riser motions on the wellhead fatigue. Specifically this methodology incorporates the generation of a detailed global finite element model of the riser and wellhead system to include detailed non-linear riser tensioner system models, accurate models of the wellhead and conductor, detailed non-linear soil response characteristics and the use of more refined current data as input to VIV calculations. The details of the riser and wellhead system model are presented and the conservatisms associated with traditional modeling methods with regard to VIV and riser tensioner load variations are discussed. A number of case studies are presented to illustrate the effects of various data assumptions and simplifications on estimated wellhead fatigue.

scholarly journals Using a multi-objective genetic algorithm for SVM construction

2006 ◽  
Vol 8 (2) ◽  
pp. 125-139 ◽  
Author(s):  
Orazio Giustolisi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Linear Regression ◽  
Environmental Data ◽  
Support Vector ◽  
Optimal Dimension ◽  
Multi Objective ◽  
Kernel Parameter ◽  
Vector Machines ◽  
Non Linear

Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support Vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural framework for non-linear regression in order to emphasise the training strategy for support vector machines and to better explain the multi-objective approach in support vector machines' construction. A support vector machine's performance depends on the kernel parameter, input selection and ε-tube optimal dimension. These will be used as decision variables for the evolutionary strategy based on a Genetic Algorithm, which exhibits the number of support vectors, for the capacity of machine, and the fitness to a validation subset, for the model accuracy in mapping the underlying physical phenomena, as objective functions. The strategy is tested on a case study dealing with groundwater modelling, based on time series (past measured rainfalls and levels) for level predictions at variable time horizons.

scholarly journals Evaluation of the Combined Effect of Mobility and Seasonality on the COVID-19 Pandemic: A Lombardy-Based Study

2021 ◽  
Author(s):  
Yuri Falzone ◽  
Luca Bosco ◽  
Giacomo Sferruzza ◽  
Tommaso Russo ◽  
Marco Vabanesi ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Significant Role ◽  
Linear Models ◽  
Human Mobility ◽  
Additive Model ◽  
Environmental Variable ◽  
Environmental Data ◽  
Combined Effect ◽  
Uv Index ◽  
Non Linear

Abstract Restrictions to human mobility had a significant role in limiting SARS-CoV-2 spread. It has been suggested that seasonality might affect viral transmissibility. Our study retrospectively investigates the combined effect that seasonal environmental factors and human mobility played on transmissibility of SARS-CoV-2 in Lombardy, Italy, in 2020.Environmental data were collected from accredited open-source web services. Aggregated mobility data for different points of interests were collected from Google Community Reports. The Reproduction number (Rt), based on the weekly counts of confirmed symptomatic COVID-19, non-imported cases, was used as a proxy for SARS-CoV-2 transmissibility. Assuming a non-linear correlation between selected variables, we used a Generalized Additive Model (GAM) to investigate with univariate and multivariate analyses the association between seasonal environmental factors (UV-index, temperature, humidity, and atmospheric pressure), location-specific mobility indices, and Rt. UV-index was the most effective environmental variable in predicting Rt. An optimal two-week lag-effect between changes in explanatory variables and Rt was selected. The association between Rt variations and individually taken mobility indices differed: Grocery & Pharmacy, Transit Station and Workplaces displayed the best performances in predicting Rt when individually added to the multivariate model together with UV-index, accounting for 85.0%, 85.5% and 82.6% of Rt variance, respectively. According to our results, both seasonality and social interaction policies played a significant role in curbing the pandemic. Non-linear models including UV-index and location-specific mobility indices can predict a considerable amount of SARS-CoV-2 transmissibility in Lombardy during 2020, emphasizing the importance of social distancing policies to keep viral transmissibility under control, especially during colder months.

scholarly journals Associations between environmental covariates and malaria incidence in high transmission settings of Uganda: A distributed non-linear lagged ecological analysis

2021 ◽  
Author(s):  
Jaffer Okiring ◽  
Isobel Routledge ◽  
Adrienne Esptein ◽  
Jane F. Namuganga ◽  
Emmanuel V. Kamya ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Malaria Transmission ◽  
Malaria Incidence ◽  
Environmental Data ◽  
Environmental Covariates ◽  
High Transmission ◽  
Monthly Temperature ◽  
Rate Of Increase ◽  
Catchment Areas ◽  
Non Linear

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in the incidence of symptomatic malaria. The study aim was to investigate the associations between environmental covariates and malaria incidence in high transmission settings of Uganda.Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period (January 2019 - December 2020). Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthy average measures of temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed non-linear lagged model was used to investigate the quantitative relationship between environmental covariates and malaria incidence. Results Overall, the median (range) monthly temperature was 30oC (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). A non-linear relationship between environmental covariates and malaria incidence was observed. An average monthly temperature of 35oC was associated with significant increases in malaria incidence compared to the median observed temperature (30oC) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the cumulative increases in MI significantly at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag month 4. An average monthly rainfall of 200mm was associated with significant increases in malaria incidence compared to the median observed rainfall (133mm) at lag month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the cumulative IRR increases of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag month 4. An average NVDI of 0.72 was associated with significant cumulative increases in IRR of malaria as compared to the median observed NDVI (0.66) at month lag 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag month 4. The rate of increase in cumulative IRR of malaria was highest within lag months 1-2 as compared to lag months 3-4 for all the environmental covariates.Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with cumulative increases in the incidence of malaria, with peak associations occurring after variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

B. The Non-Linear Calculations for Pulsating Stars

1967 ◽  
Vol 28 ◽  
pp. 105-176
Author(s):  
Robert F. Christy
Keyword(s):  
Major Part ◽  
Afternoon Session ◽  
Discussion Session ◽  
Pulsating Stars ◽  
Fourth Symposium ◽  
Non Linear ◽  
Considerable Discussion ◽  
Informal Approach ◽  
Preceding Session

(Ed. note: The custom in these Symposia has been to have a summary-introductory presentation which lasts about 1 to 1.5 hours, during which discussion from the floor is minor and usually directed at technical clarification. The remainder of the session is then devoted to discussion of the whole subject, oriented around the summary-introduction. The preceding session, I-A, at Nice, followed this pattern. Christy suggested that we might experiment in his presentation with a much more informal approach, allowing considerable discussion of the points raised in the summary-introduction during its presentation, with perhaps the entire morning spent in this way, reserving the afternoon session for discussion only. At Varenna, in the Fourth Symposium, several of the summaryintroductory papers presented from the astronomical viewpoint had been so full of concepts unfamiliar to a number of the aerodynamicists-physicists present, that a major part of the following discussion session had been devoted to simply clarifying concepts and then repeating a considerable amount of what had been summarized. So, always looking for alternatives which help to increase the understanding between the different disciplines by introducing clarification of concept as expeditiously as possible, we tried Christy's suggestion. Thus you will find the pattern of the following different from that in session I-A. I am much indebted to Christy for extensive collaboration in editing the resulting combined presentation and discussion. As always, however, I have taken upon myself the responsibility for the final editing, and so all shortcomings are on my head.)

A decomposition method in non-linear programmm

Optimization ◽  
1975 ◽  
Vol 6 (4) ◽  
pp. 549-559
Author(s):  
L. Gerencsér
Keyword(s):  
Decomposition Method ◽  
Non Linear
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