Parameters For Locations Research Articles

Research Catalog of Inland Seismicity in the Southern Korean Peninsula from 2012 to 2021 Using Deep Learning Techniques

Abstract A seismicity catalog spanning 2012–2021 is proposed for the inland and near-coastal areas of the southern Korean Peninsula (SKP). Using deep learning (DL) techniques combined with conventional methods, we developed an integrated framework for compiling a comprehensive seismicity catalog. The proposed DL-based framework allowed us to process, within a week, a large volume of data (spanning 10 yr) collected from more than 300 seismic stations. To improve the framework’s performance, a DL picker (i.e., EQTransformer) was retrained using the local datasets from the SKP combined with globally obtained data. A total of 66,858 events were detected by phase association using a machine learning algorithm, and a DL-based event discrimination model classified 29,371 events as natural earthquakes. We estimate source information more precisely using newly updated parameters for locations (a 1D velocity model and station corrections related to the location process) and magnitudes (a local magnitude equation) based on data derived from the application of the DL picker. Compared with a previous catalog, the proposed catalog exhibited improved statistical completeness, detecting 21,475 additional earthquakes. With the newly detected and located earthquakes, we observed the relative low seismicity in the northern SKP, and the linear trends of earthquakes striking northeast–southwest (NE–SW) and northwest–southeast (NW–SE) with a near-right angle between them. In particular, the NE–SW trend corresponds to boundaries of major tectonic regions in the SKP that potentially indicates the development of fault structures along the boundaries. The two predominant trends slightly differ to the suggested optimal fault orientations, implying more complex processes of preexisting geological structures. This study demonstrates the effectiveness of the DL-based framework in analyzing large datasets and detecting many microearthquakes in seismically inactive regions, which will advance our understanding of seismotectonics and seismic hazards in stable continental regions.

A Review for Green Energy Machine Learning and AI Services

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.

DESIGN OF A PRECISE FSO NETWORK INCLUDING IMPROVED MODIFIED REFRACTIVE INDEX STRUCTURE PROFILING FOR SHIMLA CITY, INDIA

The most commonly used altitude-dependent Hufnagel-Valley (HV) model for Refractive-Index Structure Parameter (C<sub>n</sub><sup>2</sup>) calculation has limited accuracy due to the reason of not considering various meteorological parameters for mountainous locations which have negative influence on optical signals. Although altitude and turbulent winds (due to temperature and pressure variations) are significant parameters giving rise to atmospheric turbulences intensity but the presence of water droplets and snowflakes in humid climate of mountainous locations play major role in reducing optical visibility, so should not be ignored. We have included the effect of snowfall in existing HV model while calculating path loss due to atmospheric turbulences to estimate these effects in Shimla city. The computed results show considerable decrement in Q-factor due to the additional losses imposed by snowflakes to the laser beam. Enlargement in effects of absorption and scattering is also observed in bit error rate (BER) results. So, the proposed modified model presents more accurate estimation of the refractive index profile as it takes more parameters into consideration responsible for atmospheric turbulence.

Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.

Generalized Space Time Autoregressive Integrated Moving Average with Exogenous (GSTARIMA-X) Models

Space time data is data that relates to events at previous times and locations. One of the models that used to analyze space time data is the Generalized Space Time Autoregressive Moving Average (GSTARMA) model. GSTARMA model uses parameters p and λk to represent the time order and spatial order respectively. Hence, this model can be called GSTARMA(p, λk ). Because in fact there are more models with different parameters for different locations, GSTARMA(p, λk ) model is more realistic. If space time data is not stationary, a different order is added so that the model becomes GSTARIMA(p, d, λk ). In modelling sometimes the accuracy of the model can increase by other influence variables. These variables are named exogenous variables. The GSTARIMA model with exogenous variables is named the GSTARIMA-X(p, d, λk ) model. This research aims to examine the model and its estimation of parameters. Model parameters are estimated using the Generalized Least Square (GLS) method. The method in this writing is literature study obtained from several articles, journals, and books that support in achieving research. The results of the study show that with the GLS method, parameter estimates can be obtained with the assumption of time order 1 and spatial order 1.

Genetic Algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code.

This data article presents the structure of a Genetic Algorithm model written in a MATLAB code for finding the 1D JONSWAP spectra parameters when measured raw spectra is not available. The JONSWAP spectra is widely used in Coastal, Offshore, and Ocean Engineering for determining wave parameters for structure designing and numerical modeling. However, finding proper spectra parameters may be difficult because of the limitations of parameterized equations to do so and the high non-linear relation between the alpha and gamma coefficients. This GA model can find the alpha and gamma parameters for specific locations considering sea-state's evolution and water-depth transitions. The application of the GA model of this data article is shown in Rueda-Bayona et al. [1].

Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations

Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on global irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Köppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Köppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based global irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Köppen-Geiger zone models.

Evaluation of reference evapotranspiration methods and sensitivity analysis of climatic parameters for sub-humid sub-tropical locations in western Himalayas (India)

ABSTRACT The estimation of crop evapotranspiration (ETc) uses reference evapotranspiration (ET0) as a key variable. Physically based FAO-56 Penman–Monteith method provides accurate ET0 estimates; however, various methods are still used in different climatic regions worldwide, either due to simplicity of the methods or due to insufficiency of data required for Penman–Monteith method. The present study is conducted at sub-humid sub-tropical locations of western Himalayan agro-climate in the Indian state of Himachal Pradesh. Performance of 12 ET0 methods, representing combination type, pan evaporation, radiation and temperature based, is compared with Penman–Monteith method. Different ET0 methods are ranked on the basis of quantitative comparison performed on mean daily ET0 obtained for a period of 6 years. The temperature-based methods performed better than radiation and pan evaporation-based methods. Linear regression equations are developed between Penman–Monteith ET0 (dependent variable) and ET0 estimates from alternate method (independent variable). Relative significance of meteorological factors essential for accurate ET0 estimation in the climate considered is investigated using sensitivity analysis. Solar radiation is identified as the most sensitive parameter for ET0 estimation in the climate of the study area, followed by maximum temperature and relative humidity.

Adaptive neuro-fuzzy approach for modeling equilibrium speed–density relationship

ABSTRACTThis paper endeavors to model the equilibrium speed–density relationship using a new fuzzy logic-based approach. To capture the randomness in traffic flow dynamics, it develops a single-input Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with a hybrid algorithm. Furthermore, it proposes a new Premise-Consequent Conjugate Effect (PCCE) relationship to estimate fundamental diagram (FD) parameters from the ANFIS model. Several options (i.e. optimal split of data into training and testing data, selection of suitable membership function) offered by ANFIS are then illustrated. An experiment is performed to determine the maximum achievable goodness of fit without the occurrence of overfitting phenomenon by changing the number of clusters. The calibrated ANFIS model is compared against traditional and advanced speed–density models for five different freeway locations. Results show that the proposed model outperforms other preceding models by attaining goodness-of-fit values within a range of 0.82–0.92. Finally, the proposed PCCE relationship shows the ANFIS model's robustness in accurate estimation of FD parameters for all freeway locations.

Sensitivity and uncertainty analysis of the HYDRUS-1D model for root water uptake in saline soils

A variance-based global sensitivity analysis (extended Fourier amplitude sensitivity test, EFAST) was applied to the Feddes module of the HYDRUS-1D model, and the sensitivity indices including both main and total effects of actual root water uptake (RWUa) to seven Feddes parameters were quantified at different growth stages of sunflower (Helianthus annuus L.): seedling, bud, flowering and maturity. The effects of soil salinity, climate conditions, and crop root growth on parameter sensitivity were explored by analysing three precipitation frequencies and two maximum root depths across four field locations with different soil salinity levels in China’s sunflower-growing regions. Uncertainties for RWUa were evaluated at four stages with varying Feddes parameters for different field locations, precipitation frequencies and maximum root depths. We found that the water stress factor concerning ceasing root water uptake (h4), and two salt stress factors ht and Sp, indicating the salinity threshold and the slope of the curve determining the fractional decline in root water uptake per unit increase in salinity below the threshold, respectively, were three most important Feddes parameters for RWUa estimation in HYDRUS-1D. In addition, the effects of soil salinity and precipitation frequencies were stronger than maximum root depth on the order of the parameters’ impacts on RWUa. Our study suggested that h1, h2, h3h, and h3l might be determined by an economical method (e.g. literature review) in saline soils with limited observations, but it is better to calibrate wilting point (h4) and salt stress parameters (ht and Sp) based on local measurements.

A Direct Overparameterize and Optimize Method for Stratigraphically Consistent Assisted History Matching of Object-Based Geomodels: Algorithm and Field Application

Summary Object-based static models are typically constructed for stratigraphically complex reservoirs. In this approach, the stratigraphic architecture is represented by use of distinct objects with specific geometric attributes and petrophysical characteristics. It is a major challenge to condition such models to production data while simultaneously maintaining the geologic realism and static conditioning. A novel work flow is developed for the assisted history matching (AHM) of object-based geomodels where the uncertain object locations and attributes are directly modified without resorting to (post-geomodeling) reparameterization techniques. It contains an object-modeling algorithm for channels and levees that provides direct access to the preraster geomodeling parameters for individual object locations and attributes. These parameters are gradually modified subject to physical constraints to achieve a history match. A fully integrated protocol is used in this process that automatically couples the static modeling algorithm with the reservoir simulator. The resulting work flow is moderated by a massively parallel and highly efficient iterative data-integration algorithm. In the AHM work flow, static and dynamic conditioning operations are respectively driven by separate objective functions and are performed at the iteration level in a sequential fashion. The static-conditioning step may add and remove objects in the geomodel, which changes the number of active AHM parameters over the course of the iterative search. The work flow handles such operations with minimal impact on the robustness of the search. A potential application of the direct AHM work flow for object-based geomodels is the identification of locations and attributes of channels in deepwater turbidite reservoirs, where the channels are typically below the resolution of the seismic data; the well spacing is typically larger than the characteristic object dimension, yet the production data exhibit strong sensitivity to channel connectivity. The concept of gradually adjusting the channel locations with the information in the production data (while maintaining static conditioning) is demonstrated on a real data set for a deepwater channelized-turbidite reservoir. The models proposed by the new AHM work flow not only improve the difficult-to-history-match injected-gas-breakthrough profiles but also provide geologically based explanations for them, taking into account the channel connectivity. The proposed AHM work flow ensures consistency across static and dynamic models by integrating multidisciplinary data with an easily auditable and replicable capability.

Developing a novel risk-based methodology for multi-criteria decision making in marine renewable energy applications

Research and development of alternative energy resources such as wave energy has always attracted significant attention due to their abundant and sustainable nature. The uncertainties associated with the marine environment and the significant costs required for implementation of Wave Energy Converters (WECs) require a sound decision making methodology. This paper presents a novel risk-based methodology for selecting sites for WEC installation to minimize the overall economic risk. It provides WEC developers, investors, governments and policy makers a methodology for evaluating influencing parameters for potential site locations whilst also optimizing wave energy extraction. A Bayesian network is developed to model the probabilistic influencing parameters and then it is extended to an influence diagram for estimating the expected utility of installing the WEC equipment in a selected location. To demonstrate the application of the developed methodology, three sites in the south coast of Tasmania are considered. Based on actual sea state data, the optimum location for installing WEC equipment is determined as location 2 and the economic risk associated with energy extraction is minimized by suggesting a specific wave height (HS = 5 m) as a design criteria.

Fracture caused by wedging in a thermoelastic solid

ABSTRACTDynamic steady-state fracture in 3D of an unbounded, coupled thermoelastic solid is considered. Fracture is brittle and results from the wedging effect of a rigid die that slides between surfaces of an initially closed, semi-infinite slit. Friction is neglected, and the possibility that slit surfaces resume contact in the wake of the die is considered. For subcritical rate of sliding and resultant slit extension, an asymptotic solution is obtained in analytic form. A dynamic energy release rate criterion, with kinetic energy included, is imposed. The result is a relation between parameters for locations of slit edge, slit closure, and contact zone, in the form of a nonlinear differential equation. The mathematical norm of temperature change is also developed and indicates that thermal response near the slit edge is highly dependent on the relative distances of slit edge and closure contour from the die.

Sensitivity and Error Analysis of a Coupled Micro-Resonator Array for Ultra-Sensitive Mass Detection Using Matrix Perturbation Theory

A coupled micro-resonator array of cantilevers presents a cyclic symmetrical eigen system, which exhibits a characteristic modal spectrum. However, addition of even a small mass can perturb the modal characteristics significantly and this can be employed to design an ultrasensitive mass sensor. Researchers have investigated a varied range of micro-resonator arrays and have shown that shift in eigenvalues and eigenvectors in the modified spectrum depends on the target mass and its location. However, exact formulation of sensitivity as a calibration constant for a multi-resonator array in general has not been presented. In addition, error analysis, which is an important aspect of any sensor design, has not been studied so far. In this paper, matrix perturbation theory is employed to derive the sensor sensitivity parameters for different target locations and different eigenshift measurements. An optimum sensor array for maximum sensitivity is selected, and error analysis is done for the selected resonator to decide a threshold of target mass for a given error bound. Numerical simulation study shows that interconnection stiffness does not affect the sensitivity as derived, but it affects error of mass detection.

Effects of Heating Location and Size on Natural Convection in Partially Heated Open-Ended Enclosure by Using Lattice Boltzmann Method

Natural convection heat transfer in an square enclosure, consisting of a partially heated west wall with east end open to ambient, is investigated numerically by using an in-house computational fluid dynamics solver based on thermal lattice Boltzmann method. In particular, the influences of Rayleigh number (103–106), heating location (bottom, middle, and top) on west wall, and dimensionless heating length (0.25–0.75) on momentum and heat transfer characteristics of air are presented and discussed. The streamline patterns show bifurcation at the lowest Rayleigh number for bottom and middle heating, whereas at the highest Rayleigh number, all heating positions yield bifurcation and elongation of flow patterns with a secondary vortex near the lower side of open end. The middle and bottom heating locations show a linear increase in Nusselt number with heater size, whereas inverse dependence is seen for top heating. The maximum heat transfer is observed in the case of middle heating. As expected, average Nusselt number increased with increasing Rayleigh number. Finally, the functional dependence of the average Nusselt number on flow governing parameters (Rayleigh number and heating length) for different heating locations is presented as a simple predictive empirical relationship.

New Location Selection Criterions for Solar PV Power Plant

India is the seventh largest country in the world and blessed with adequate solar radiation, therefore to setting up a Photovoltaic power plant is a lucrative option. Indian climatic conditions vary enormously from east to west and north to south due to its geographical position in the world map. It is also observed that in actual field conditions the performance of the PV modules vary significantly when compared to Standard Test Condition (STC) due to large change in environmental conditions. Now it is very important to study the exact meteorological parameters for different locations in India before installation of a Photovoltaic power plant. In this paper, the long-term meteorological parameters for the seventy considered locations in India from National Aeronautics and Space Administration (NASA) renewable energy resource website (Surface Meteorology and Solar Energy) are collected and analyzed in order to study the behaviour of solar radiation, insolation on an equator-pointed surface that is tilted at latitude angle, insolation clearness index, daylight hours, air temperature and relative humidity of India to select an ideal location for installation of PV power plant. The analysis of the paper helps the investors, Ministry of New and Renewable Energy (MNRE), Electricity Authority and Planning Commission to know which locations in India are environmentally ideal for installation of PV power plant.

A New Approach towards Ideal Location Selection for PV Power Plant in India

India is the seventh largest country in the world, blessed with adequate solar radiation, therefore to setting up a Photovoltaic power plant is a lucrative option. In actual field conditions the performance of the PV modules vary significantly from its Standard Test Condition (STC) due to large change in environmental conditions. It is very important to study the exact meteorological parameters for different locations in India before installation of a Photovoltaic power plant. In this paper, the long-term meteorological parameters for the seventy considered locations in India from National Aeronautics and Space Administration (NASA) renewable energy resource website (Surface Meteorology and Solar Energy) are collected and analyzed in order to select an ideal location for installation of PV power plant. This paper helps the investors, Ministry of New and Renewable Energy (MNRE), Electricity Authority and Planning Commission to select proper locations for installation of PV power plant.

Analytical and statistical evaluation of surface and sub- surface water of BALCO industrial area, Korba, C.G., India

We have profoundly undertaken assessment of water quality; characterized by physical, chemical and biological properties with selected metallic elements of surface and subsurface water sources from Balco industrial area. Ten sampling spots i.e. four for GW and six from SW bodies were selected and assigned BK1 to BK10. Water samples were taken in 2L capacity of polyethylene bottles. The parameters chosen are Temp., pH, EC, Turb., Dissolved and Suspended solids, Acidity, Alkalinity, TH and Metallic elements like Fe, Al, Mn, As and Zn. The result were compared with standard values stipulated by WHO, BIS, ISI: 10500:19991 for drinking water. The observed values for different qualities were analysed using the statistical parameters such as mean, SD, SE, %CV, correlation coefficient r value and WQI. Highest strong positive and negative correlation were recorded for GW and SW between TS vs TDS (r = 0.960 and 0.939), Turb. vs pH (r = 0.663) and T. Aci. (r = 0.872) respectively. Max. % of CV for GW and SW noted for As 106.89 and 149.10, indicating high variation of two parameters for different locations. WQI measured in order of BS5 > BS6 > BS10 > BS2 > BS3> BS7 > BS4 > BS9 > BS8> BS1.

Multiple Balance Strategies for Humanoid Standing Control

Full-state feedback parametric controllers are proposed for standing balance of humanoid robots in response to impulsive and constant pushes. Multiple robot models are used to approach multiple strategies in human standing balance. For each model, we design a parametric controller acting on each state variable and optimize controller parameters for different push sizes, directions, and locations. The performance of each controller is shown in response to different external pushes. By comparing the capabilities of handling disturbances in each strategy, the contributions of every joint to standing balance are also explored.

Alkane and PAH biomarkers as tracers of terrigenous organic carbon in Arctic Ocean sediments

We present the first sedimentary biomarker study encompassing the entire Arctic Ocean. A large data set of organic markers for terrigenous, petroleum and combustion inputs [alkanes, hopanes and steranes, parent and alkyl polycyclic aromatic hydrocarbons (PAHs)] is examined for patterns in space and time using principal components analysis (PCA) and partial least squares (PLS). Biomarker patterns reveal the central Arctic Ocean basin sediments to be compositionally distinct from those of the Mackenzie River/Beaufort Sea and Barents Sea, but similar to those of the Laptev Sea. PAH distributions reflected in PAH ratios and PCA projections demonstrate that Arctic Ocean sediment is dominated by natural inputs to the extent that anthropogenic combustion PAHs are not significant. We find only modest changes between the glacial and post-glacial sediments for atmospherically transported hydrocarbon biomarkers, while particle associated biomarkers were captured strongly at basin edges during the glacial period, and much more evenly transported across basins during the post-glacial period. The orders of magnitude decreases in particle associated petrogenic alkanes and PAHs in central basins during glacial times, coupled with the uniformity of most petrogenic biomarker parameters for most basin and shelf locations, reflect a massive reduction in ice transport that makes the margins the most likely source of petrogenic material for the Pleistocene/Holocene central Arctic basins. The proximity of large coal deposits of various maturity levels along the Lena River, the overlap in PAH and biomarker composition of the Laptev Sea and surficial sediments from the central Arctic Ocean and the location of the Laptev Sea at the origin of the main Transpolar Drift all point to eroded coals from the Lena River/Laptev Sea as the likely source of petrogenic hydrocarbons to the central Arctic Ocean. The ubiquitous presence of allochthonous coal in Arctic Ocean surface sediments provides a major constraint on the use of petrogenic biomarkers to infer the presence of subsurface petroleum reserves.