A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

Variogram models are a valuable tool used to analyze the variability of a time series; such variability usually entails a spherical or exponential behavior, and so, models based on such functions are commonly used to fit and explain a time series. Variograms have a quasi-periodic structure for rainfall cases, and some extra steps are required to analyze their entire behavior. In this work, we detailed a procedure for a complete analysis of rainfall time series, from the construction of the experimental variogram to curve fitting with well-known spherical and exponential models, and finally proposed a novel model: quadratic–exponential. Our model was developed based on the analysis of 6 out of 30 rainfall stations from our case study: the Río Bravo–San Juan basin, and was constructed from the exponential model while introducing a quadratic behavior near to the origin and taking into account the fact that the maximal variability of the process is known. Considering a sample with diverse Hurst exponents, the stations were selected. The results obtained show robustness in our proposed model, reaching a good fit with and without the nugget effect for different Hurst exponents. This contrasts to previous models, which show good outcomes only without the nugget effect.

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An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM)

Benchmarking An International Journal ◽

10.1108/bij-08-2013-0087 ◽

2016 ◽

Vol 23 (6) ◽

pp. 1398-1422 ◽

Cited By ~ 18

Author(s):

Luís Miguel D. F. Ferreira ◽

Cristóvão Silva ◽

Susana Garrido Azevedo

Keyword(s):

Supply Chain ◽

Supply Chains ◽

Performance Management ◽

Balanced Scorecard ◽

Environmental Performance ◽

Supply Chain Performance ◽

Content Type ◽

Proposed Model ◽

Novel Model

Purpose – Companies need to excel in many areas to achieve a competitive advantage. This, together with pressure imposed by regulators and customers regarding sustainability concerns, leads companies to address sustainability in an integrated fashion across all management processes. The purpose of this paper is to suggest a model for the assessment of the environmental performance of a supply chain, based on four perspectives used in the balanced scorecard. Performance indicators are proposed based on the literature, as well as on the ISO 14031 and GRI standards, and were validated by a panel of experts. Design/methodology/approach – Based on a literature review on models for environmental performance management a novel model to assess the environmental performance of the supply chains (Env_BSC_4_SCPM) is proposed. Data collected from the first tier suppliers of an automotive industry case study are used to test the proposed model. Findings – The model developed was tested in a case study company, showing it ability to benchmark the company first tiers suppliers and products. The model is also useful as a decision support tool to define actions to be taken in order to improve the global environment performance of the supply chain. Research limitations/implications – The proposed model was developed to evaluate the environmental performance of supply chains. Nevertheless, the case study only takes account of the first tier suppliers, due to difficulties associated to data collecting for the other elements in the supply chain. Widening the frontiers, the next phase may include the application of this model to second, third and lower tier suppliers, as well as the final customer. Improvements in the model could also include the construction of a composite index to measure the environmental supply chain performance. Practical implications – The paper provides a model that can be used by practitioners to evaluate the environmental performance of their supply chain and to decide on actions to be taken to improve it. Originality/value – As stated by several authors, there has been limited research conducted in the field of environmental evaluation of supply chains. This paper proposes a novel model for the environmental performance of the supply chain and tests it using industrial empirical data.

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The Associative Multifractal Process: A Novel Model for Computer Network Traffic Flows

10.36227/techrxiv.14842413 ◽

2021 ◽

Author(s):

Ginno Millán ◽

Gastón Lefranc ◽

Román Osorio-Comparán

Keyword(s):

Mathematical Model ◽

Time Series ◽

High Speed ◽

Computer Network ◽

Traffic Flows ◽

Multifractal Formalism ◽

Proposed Model ◽

Self Similar ◽

Speed Computer ◽

Novel Model

A novel constructive mathematical model based on the multifractal formalism in order to accurately characterizing the localized fluctuations present in the course of traffic flows today high-speed computer networks is presented. The proposed model has the target to analyze self-similar second-order time series representative of traffic flows in terms of their roughness and impulsivity.

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Estimation of deterministic component of monthly rainfall time series : A case study for Pantnagar

MAUSAM ◽

10.54302/mausam.v69i3.338 ◽

2021 ◽

Vol 69 (3) ◽

pp. 449-458

Author(s):

MANISHA MADHAV NAVALE ◽

P. S. KASHYAP ◽

SACHIN KUMAR SINGH ◽

DANIEL PRAKASH KUSHWAHA ◽

DEEPAK KUMAR ◽

...

Keyword(s):

Time Series ◽

Monthly Rainfall ◽

Rainfall Time Series ◽

Deterministic Component

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A Data-Driven Surrogate Modelling Approach for Acceleration of Short-Term Simulations of a Dynamic Urban Drainage Simulator

Water ◽

10.3390/w10121849 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1849 ◽

Cited By ~ 2

Author(s):

Mahmood Mahmoodian ◽

Jairo Arturo Torres-Matallana ◽

Ulrich Leopold ◽

Georges Schutz ◽

Francois H. L. R. Clemens

Keyword(s):

Time Series ◽

Storage Tank ◽

Data Driven ◽

Urban Drainage ◽

Rainfall Time Series ◽

Short Term ◽

Time Step ◽

Speed Up ◽

Error Indicators

In this study, applicability of a data-driven Gaussian Process Emulator (GPE) technique to develop a dynamic surrogate model for a computationally expensive urban drainage simulator is investigated. Considering rainfall time series as the main driving force is a challenge in this regard due to the high dimensionality problem. However, this problem can be less relevant when the focus is only on short-term simulations. The novelty of this research is the consideration of short-term rainfall time series as training parameters for the GPE. Rainfall intensity at each time step is counted as a separate parameter. A method to generate synthetic rainfall events for GPE training purposes is introduced as well. Here, an emulator is developed to predict the upcoming daily time series of the total wastewater volume in a storage tank and the corresponding Combined Sewer Overflow (CSO) volume. Nash-Sutcliffe Efficiency (NSE) and Volumetric Efficiency (VE) are calculated as emulation error indicators. For the case study herein, the emulator is able to speed up the simulations up to 380 times with a low accuracy cost for prediction of the total storage tank volume (medians of NSE = 0.96 and VE = 0.87). CSO events occurrence is detected in 82% of the cases, although with some considerable accuracy cost (medians of NSE = 0.76 and VE = 0.5). Applicability of the emulator for consecutive short-term simulations, based on real observed rainfall time series is also validated with a high accuracy (NSE = 0.97, VE = 0.89).

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A hybrid linear–nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model

Journal of Hydroinformatics ◽

10.2166/hydro.2017.013 ◽

2017 ◽

Vol 20 (1) ◽

pp. 246-262 ◽

Cited By ~ 17

Author(s):

Jamileh Farajzadeh ◽

Farhad Alizadeh

Keyword(s):

Time Series ◽

Urmia Lake ◽

Support Vector ◽

Discrete Wavelet ◽

Rainfall Time Series ◽

Model Based ◽

Nonlinear Approach ◽

Proposed Model ◽

Model Time Series ◽

Predicted Values

Abstract The present study aimed to develop a hybrid model to predict the rainfall time series of Urmia Lake watershed. For this purpose, a model based on discrete wavelet transform, ARIMAX and least squares support vector machine (LSSVM) (W-S-LSSVM) was developed. The proposed model was designed to handle linear, nonlinear and seasonality of rainfall time series. In the proposed model, time series were decomposed into sub-series (approximation (a) and details (d)). Next, the sub-series were predicted separately. In the proposed model, sub-series were fed into SARIMAX to be predicted. The residual of predicted sub-series (error) of the rainfall time series was then fed into LSSVM to predict the residual components. Then, all predicted values were aggregated to rebuild the predicted time series. In order to compare results, first a classic modeling was performed by LSSVM. Later, wavelet-based LSSVM was used to capture the peak values of rainfall. Results revealed that Daubechies 4 and decomposition level 4 (db(4,4)) led to the best outcome. Due to the performance of db(4,4), it was selected to be applied in the proposed model. Based on results, it was observed that the W-S-LSSVM's performance was improved in comparison with other models.

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The Associative Multifractal Process: A Novel Model for Computer Network Traffic Flows

10.36227/techrxiv.14842413.v1 ◽

2021 ◽

Author(s):

Ginno Millán ◽

Gastón Lefranc ◽

Román Osorio-Comparán

Keyword(s):

Mathematical Model ◽

Time Series ◽

High Speed ◽

Computer Network ◽

Traffic Flows ◽

Multifractal Formalism ◽

Proposed Model ◽

Self Similar ◽

Speed Computer ◽

Novel Model

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Influence of the length of rainfall time series on rainwater harvesting systems: A case study in Berlin

Resources Conservation and Recycling ◽

10.1016/j.resconrec.2017.06.011 ◽

2017 ◽

Vol 125 ◽

pp. 169-180 ◽

Cited By ~ 9

Author(s):

Matheus Soares Geraldi ◽

Enedir Ghisi

Keyword(s):

Time Series ◽

Rainwater Harvesting ◽

Rainfall Time Series ◽

Harvesting Systems

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Probabilistic reference model for hourly PV power generation forecasting

E3S Web of Conferences ◽

10.1051/e3sconf/202015201002 ◽

2020 ◽

Vol 152 ◽

pp. 01002

Author(s):

L. Alfredo Fernandez-Jimenez ◽

Sonia Terreros-Olarte ◽

Alberto Falces ◽

Pedro M. Lara-Santillan ◽

Enrique Zorzano-Alba ◽

...

Keyword(s):

Time Series ◽

Reference Model ◽

Power Production ◽

Forecasting Model ◽

Probabilistic Forecasting ◽

Mean Power ◽

Forecasting Models ◽

Proposed Model ◽

Probabilistic Forecasts

This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

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A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data

Complexity ◽

10.1155/2021/9951817 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

I. Creus-Martí ◽

A. Moya ◽

F. J. Santonja

Keyword(s):

Time Series ◽

Maximum Likelihood ◽

Autoregressive Model ◽

Time Series Data ◽

Series Data ◽

High Dimensional ◽

Time Varying ◽

Proposed Model ◽

Time Varying Parameters

Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.

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Comparing methods to place adaptive local RTC actuators for spill volume reduction from multiple CSOs

Journal of Hydroinformatics ◽

10.2166/hydro.2021.085 ◽

2021 ◽

Author(s):

M. Eulogi ◽

S. Ostojin ◽

P. Skipworth ◽

S. Kroll ◽

J. D. Shucksmith ◽

...

Keyword(s):

Time Series ◽

Control Device ◽

Volume Reduction ◽

Rainfall Time Series ◽

Storm Events ◽

Real Time Control ◽

Location Selection ◽

Time Control ◽

Computational Resources

Abstract The selection of flow control device (FCD) location is an essential step for designing real-time control (RTC) systems in sewer networks. In this paper, existing storage volume-based approaches for location selection are compared with hydraulic optimisation-based methods using genetic algorithm (GA). A new site pre-screening methodology is introduced, enabling the deployment of optimisation-based techniques in large systems using standard computational resources. Methods are evaluated for combined sewer overflow (CSO) volume reduction using the CENTAUR autonomous local RTC system in a case study catchment, considering overflows under both design and selected historic rainfall events as well as a continuous 3-year rainfall time series. The performance of the RTC system was sensitive to the placement methodology, with CSO volume reductions ranging between −6 and 100% for design and lower intensity storm events, and between 15 and 36% under continuous time series. The new methodology provides considerable improvement relative to storage-based design methods, with hydraulic optimisation proving essential in relatively flat systems. In the case study, deploying additional FCDs did not change the optimum locations of earlier FCDs, suggesting that FCDs can be added in stages. Thus, this new method may be useful for the design of adaptive solutions to mitigate consequences of climate change and/or urbanisation.

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