Investigating the drag-based model parameters through statistical methods

2021 ◽  
Author(s):  
Gianluca Napoletano ◽  
Raffaello Foldes ◽  
Francesco Berrilli ◽  
Daniele Calchetti ◽  
Giancarlo de Gasperis ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Optimal Choice ◽  
Empirical Models ◽  
Computational Time ◽  
Forecasting Model ◽  
Model Parameters ◽  
Statistical Distributions ◽  
Space Weather Forecasting ◽  
Forecasting Models ◽  
Small Set

<p>Due to their simplicity and relatively short computational time, empirical models for Solar Wind Transients, based on a restricted number of assumptions and on the values of a small set of parameters, play an important role in Space Weather forecasting. For this reason, an optimal choice of values for the model parameters is of critical importance in this approach. In this work, we compiled a list of CME events by merging and cross-referencing several databases and made use of such experimental data to evaluate statistical distributions for the model parameters of a chosen forecasting model for ICME arrivals, namely the Drag-Based model. Our results lead to several considerations and refinements to be implemented in the future in this and other forecasting models.</p>

Fuzzy grey forecasting model optimized by moth-flame optimization algorithm for short time electricity consumption

2021 ◽  
pp. 1-10
Author(s):  
Ceyda Tanyolaç Bilgiç ◽  
Boğaç Bilgiç ◽  
Ferhan Çebi
Keyword(s):  
Optimization Algorithm ◽  
Electricity Consumption ◽  
Grey Model ◽  
Forecasting Model ◽  
Model Parameters ◽  
Forecasting Accuracy ◽  
Forecasting Models ◽  
True Value ◽  
Proposed Model ◽  
Moth Flame Optimization Algorithm

It is significant that the forecasting models give the closest result to the true value. Forecasting models are widespread in the literature. The grey model gives successful results with limited data. The existing Triangular Fuzzy Grey Model (TFGM (1,1)) in the literature is very useful in that it gives the maximum, minimum and average value directly in the data. A novel combined forecasting model named, Moth Flame Optimization Algorithm optimization of Triangular Fuzzy Grey Model, MFO-TFGM (1,1), is presented in this study. The existing TFGM (1,1) model parameters are optimized by a new nature- inspired heuristic algorithm named Moth-Flame Optimization algorithm which is inspired by the moths flying path. Unlike the studies in the literature, in order to improve the forecasting accuracy, six parameters (λL, λM, λR, α, β and γ) were optimized. After the steps of the model is presented, a forecasting implementation has been made with the proposed model. Turkey’s hourly electricity consumption data is utilized to show the success of the prediction model. Prediction results of proposed model is compared with TFGM (1,1). MFO-TFGM (1,1) performs higher forecasting accuracy.

scholarly journals On Novel Nonhomogeneous Multivariable Grey Forecasting Model NHMGM

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Haixia Wang ◽  
Peiguang Wang ◽  
M. Tamer Şenel ◽  
Tongxing Li
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Forecasting Model ◽  
Model Parameters ◽  
The Novel ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Nonlinear Relation ◽  
Novel Model

A novel nonhomogeneous multivariable grey forecasting model termed NHMGM(1,m,kp,c) is proposed in this paper for use in nonhomogeneous multivariable exponential data sequences. The NHMGM(1,m,kp,c) model is able to reflect the nonlinear relation of the data sequences in the system, and it is proved that many classic grey forecasting models can be derived from NHMGM(1,m,kp,c) model. Parameters of the novel model are obtained by using least square method, and the time response function is given. A numerical example is presented to show the effectiveness of the proposed model, six different grey forecasting models are built for modeling, and two popular accuracy criteria (ARPE and MAPE) are adopted to test the reliability of the novel model. The example demonstrates that NHMGM-2 model provides favorable performance compared with the other five grey models. Additionally, the multiplication transformation properties of NHMGM(1,m,kp,c) are systematically analysed, which establish a theoretical foundation for further applications of the model.

scholarly journals All Hands on Deck for Ionospheric Modeling

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Doğacan �zt�rk ◽  
Katherine Garcia-Sage ◽  
Hyunju Connor
Keyword(s):  
Space Weather ◽  
Weather Forecasting ◽  
Electrical Currents ◽  
Space Weather Forecasting ◽  
Forecasting Models ◽  
Ionospheric Modeling ◽  
Hands On

Challenges to studying the ionosphere’s ability to conduct electrical currents undercut scientists’ efforts to improve space weather forecasting models. Let’s tackle them together.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

scholarly journals Space weather: the solar perspective

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Manuela Temmer
Keyword(s):  
Solar Wind ◽  
Space Weather ◽  
Weather Forecasting ◽  
Interplanetary Space ◽  
Three Dimensions ◽  
Space Weather Forecasting ◽  
Stereo Mission ◽  
Interaction Regions ◽  
Near Future ◽  
First Time

AbstractThe Sun, as an active star, is the driver of energetic phenomena that structure interplanetary space and affect planetary atmospheres. The effects of Space Weather on Earth and the solar system is of increasing importance as human spaceflight is preparing for lunar and Mars missions. This review is focusing on the solar perspective of the Space Weather relevant phenomena, coronal mass ejections (CMEs), flares, solar energetic particles (SEPs), and solar wind stream interaction regions (SIR). With the advent of the STEREO mission (launched in 2006), literally, new perspectives were provided that enabled for the first time to study coronal structures and the evolution of activity phenomena in three dimensions. New imaging capabilities, covering the entire Sun-Earth distance range, allowed to seamlessly connect CMEs and their interplanetary counterparts measured in-situ (so called ICMEs). This vastly increased our knowledge and understanding of the dynamics of interplanetary space due to solar activity and fostered the development of Space Weather forecasting models. Moreover, we are facing challenging times gathering new data from two extraordinary missions, NASA’s Parker Solar Probe (launched in 2018) and ESA’s Solar Orbiter (launched in 2020), that will in the near future provide more detailed insight into the solar wind evolution and image CMEs from view points never approached before. The current review builds upon the Living Reviews article by Schwenn from 2006, updating on the Space Weather relevant CME-flare-SEP phenomena from the solar perspective, as observed from multiple viewpoints and their concomitant solar surface signatures.

VENDOR-NEUTRAL STOCHASTIC INVERSION OF LWD DEEP AZIMUTHAL RESISTIVITY DATA AS A STEP TOWARD EFFICIENCY STANDARDIZATION OF GEOSTEERING SERVICES

2021 ◽  
Author(s):  
Mikhail Sviridov ◽  
◽  
Anton Mosin ◽  
Sergey Lebedev ◽  
Ron Thompson ◽  
...  
Keyword(s):  
Markov Chains ◽  
Quality Indicators ◽  
Parallel Execution ◽  
Oil Field ◽  
Model Complexity ◽  
Computational Time ◽  
Model Parameters ◽  
Formation Model ◽  
Inversion Algorithm ◽  
Azimuthal Resistivity

While proactive geosteering, special inversion algorithms are used to process the readings of logging-while-drilling resistivity tools in real-time and provide oil field operators with formation models to make informed steering decisions. Currently, there is no industry standard for inversion deliverables and corresponding quality indicators because major tool vendors develop their own device-specific algorithms and use them internally. This paper presents the first implementation of vendor-neutral inversion approach applicable for any induction resistivity tool and enabling operators to standardize the efficiency of various geosteering services. The necessity of such universal inversion approach was inspired by the activity of LWD Deep Azimuthal Resistivity Services Standardization Workgroup initiated by SPWLA Resistivity Special Interest Group in 2016. Proposed inversion algorithm utilizes a 1D layer-cake formation model and is performed interval-by-interval. The following model parameters can be determined: horizontal and vertical resistivities of each layer, positions of layer boundaries, and formation dip. The inversion can support arbitrary deep azimuthal induction resistivity tool with coaxial, tilted, or orthogonal transmitting and receiving antennas. The inversion is purely data-driven; it works in automatic mode and provides fully unbiased results obtained from tool readings only. The algorithm is based on statistical reversible-jump Markov chain Monte Carlo method that does not require any predefined assumptions about the formation structure and enables searching of models explaining the data even if the number of layers in the model is unknown. To globalize search, the algorithm runs several Markov chains capable of exchanging their states between one another to move from the vicinity of local minimum to more perspective domain of model parameter space. While execution, the inversion keeps all models it is dealing with to estimate the resolution accuracy of formation parameters and generate several quality indicators. Eventually, these indicators are delivered together with recovered resistivity models to help operators with the evaluation of inversion results reliability. To ensure high performance of the inversion, a fast and accurate semi-analytical forward solver is employed to compute required responses of a tool with specific geometry and their derivatives with respect to any parameter of multi-layered model. Moreover, the reliance on the simultaneous evolution of multiple Markov chains makes the algorithm suitable for parallel execution that significantly decreases the computational time. Application of the proposed inversion is shown on a series of synthetic examples and field case studies such as navigating the well along the reservoir roof or near the oil-water-contact in oil sands. Inversion results for all scenarios confirm that the proposed algorithm can successfully evaluate formation model complexity, recover model parameters, and quantify their uncertainty within a reasonable computational time. Presented vendor-neutral stochastic approach to data processing leads to the standardization of the inversion output including the resistivity model and its quality indicators that helps operators to better understand capabilities of tools from different vendors and eventually make more confident geosteering decisions.

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