Output Data Sets Research Articles

Application of Ensemble Variance Analysis in the Development of the Wheatstone Field Startup Strategy

Summary Starting initial field production is a unique opportunity to acquire reservoir–surveillance information that can impact future reservoir performance. When a field is perturbed by first production, there is the potential to reduce uncertainty in reservoir properties by monitoring the pressure changes at nonproducing wells with downhole pressure gauges and comparing the observed signal with a range of simulation–model results. The Wheatstone Field, located offshore of Northwest Australia, recently began production startup to supply gas to the Wheatstone liquefied–natural–gas (LNG) facility. The operational guidelines required each development well to start with a single–well–cleanup flow to the Wheatstone platform. The initial single–well–cleanup flows of the Wheatstone field provided scope for selecting a well flow sequence with observation at nonproducing wells. The recommended sequence of initial cleanup flows was designed with a focus on reducing reservoir uncertainties by means of ensemble variance analysis (EVA). EVA is a statistical correlation technique that compares the covariance between two sets of output data with the same set of inputs. For the Wheatstone Field well–cleanup flow sequence selection, the EVA workflow compared the full–field design of experiments (DoE) study of field depletion and a series of short early–production reservoir–simulation DoE studies of the gas field. The covariance between the two DoE studies was evaluated. The objective of the EVA approach was to determine the startup sequence that would allow for the best opportunity to reduce subsurface uncertainty. This objective was met by ranking multiple cleanup flow sequence scenarios. The key factors considered for sequence selection ranking were the impact on business objectives, such as future–drilling–campaign timing and location of infill wells, and insights on reservoir connectivity, gas initially in place (GIIP), and permeability. The recommended sequence of well–cleanup flows uses a superpositioned pressure signal to boost response at observation wells, which improves measurement resolvability. The selected sequence preserves key observation wells for each manifold and reservoir section for as long as possible before those wells are required to be flowed to meet operational requirements. Operational constraints and variations of the startup plan were considered part of the evaluation.

Creating Truth Data to Quantify the Accuracy of Cloud Forecasts from Numerical Weather Prediction and Climate Models

Clouds are critical in mechanisms that impact climate sensitivity studies, air quality and solar energy forecasts, and a host of aerodrome flight and safety operations. However, cloud forecast accuracies are seldom described in performance statistics provided with most numerical weather prediction (NWP) and climate models. A possible explanation for this apparent omission involves the difficulty in developing cloud ground truth databases for the verification of large-scale numerical simulations. Therefore, the process of developing highly accurate cloud cover fraction truth data from manually generated cloud/no-cloud analyses of multispectral satellite imagery is the focus of this article. The procedures exploit the phenomenology to maximize cloud signatures in a variety of remotely sensed satellite spectral bands in order to create accurate binary cloud/no-cloud analyses. These manual analyses become cloud cover fraction truth after being mapped to the grids of the target datasets. The process is demonstrated by examining all clouds in a NAM dataset along with a 24 h WRF cloud forecast field generated from them. Quantitative comparisons with the cloud truth data for the case study show that clouds in the NAM data are under-specified while the WRF model greatly over-predicts them. It is concluded that highly accurate cloud cover truth data are valuable for assessing cloud model input and output datasets and their creation requires the collection of satellite imagery in a minimum set of spectral bands. It is advocated that these remote sensing requirements be considered for inclusion into the designs of future environmental satellite systems.

The Influence of Tobacco Retailer Density and Poverty on Tobacco Use in a Densely Populated Urban Environment.

Researchers have identified associations between neighborhood-level factors (eg, income level, tobacco retailer density) and smoking behavior, but few studies have assessed these factors in urban environments. We explored the effect of tobacco retailer density, neighborhood poverty, and housing type (multiunit and public) on smoking in a large urban environment (New York City). We used data on smoking prevalence and individual sociodemographic characteristics from the 2011-2013 New York City Community Health Survey, data on tobacco retailers from the 2012 New York City Department of Consumer Affairs, data on neighborhood sociodemographic characteristics and population density from the 2009-2013 American Community Survey, and data on multiunit and public housing from the 2012 New York City Primary Land Use Tax Lot Output data set. We used aggregate neighborhood-level variables and ordinary least squares regression, geographic weighted regression, and multilevel models to assess the effects of tobacco retailer density and neighborhood poverty on smoking prevalence, adjusting for sociodemographic characteristics (age, sex, race/ethnicity, and education) and neighborhood population density. We also assessed interactions between tobacco retailer density and poverty and each housing type on smoking. Neighborhood poverty positively and significantly modified the association between tobacco retailer density and prevalence of neighborhood smoking ( β = 0.003, P = .01) when we controlled for population density, sociodemographic characteristics, and types of housing. Neighborhood poverty was positively associated with the prevalence of individual smoking ( β = 0.0099, P < .001) when we adjusted for population density, sociodemographic characteristics, and type of housing. More research is needed to determine all the environmental factors associated with smoking prevalence in a densely populated urban environment.

Impacts of aluminum production: A cradle to gate investigation using life-cycle assessment

In this study, a life-cycle assessment from the aluminum production processes is carried out by considering four major steps of aluminum production - bauxite mining from the ore, alumina production, smelting, and the ingot casting process of aluminum. The study considered the cradle to gate system to analyze and quantify the environmental impacts during aluminum production process from raw materials to the final finished product. The production input and output datasets are taken from the databases and eGrid reports and the analysis is carried out utilizing three different methods, namely the International Life-cycle Reference Data System (ILCD) method, the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI) method, and the Cumulative Energy Demand (CED) method. The results show electricity consumption in aluminum smelting causes noteworthy environmental burdens among all the processes or materials involved. Residual fuel oil, diesel, and natural gas consumption during the alumina production is also a major contributor to environmental impacts. Sensitivity analysis is conducted to justify the variation of environmental impacts in proportion with the modes of electricity generation from different sources to be used for aluminum smelting. Further sensitivity analysis is carried out among the quantity of the fossil fuel used based on their sources for process heat generation during alumina production. According to the sensitivity analysis results, renewable energy integration or reducing the consumption of fossil fuels would be the promising alternative to reduce the environmental burdens associated with aluminum production.

Reliability-based optimum design of hydraulic water retaining structure constructed on heterogeneous porous media: utilizing stochastic ensemble surrogate model-based linked simulation optimization model

Seepage characteristics under hydraulic water retaining structures (HWRSs) significantly affect the hydraulic serviceability and stability of such structures. The expected hydraulic conductivity value and its spatial and directional variation substantially influence seepage characteristics. Furthermore, the uniform and homogenous hydraulic conductive is rarely seen in the real field. To study the effects of uncertainty and variation in hydraulic conductivity, a random field concept was used to generate different realizations of heterogeneous hydraulic conductivity with constant mean and varied standard deviation. Consequently, the seepage characteristics stochastically varied, creating uncertainty in seepage characteristics which influenced the HWRS design. Therefore, the objective of this paper was to integrate the reliability concept in the linked simulation optimization (S-O) model to address the uncertainty of hydraulic conductivity. Hence, the safest and most cost-effective HWRS design could be attained considering the effects of the uncertainty of hydraulic conductivity. The reliability-based optimum design (RBOD) concept was implemented utilizing the multiple realization optimization technique based on stochastic ensemble surrogate models. The reliability degree of each candidate design was measured based on the number of stochastic constraints satisfying the design requirements to the total number of constraints. The S-O model-based RBOD was formulated to find the most cost-effective HWRS design satisfying a beforehand desired degree of reliability. Each surrogate model was trained utilizing several (input–output) data sets simulated using numerical seepage modeling code (SEEPW). The Gaussian process regression machine learning technique was used to train several surrogate models to imitate the responses of the numerical seepage modeling. Each single input data set was solved (4 × 5) times to simulate four different realizations of spatial variation resulting from one of the five different standard deviation values (0.85, 1.55, 2.25, 2.95, 3.65 m/day) with constant mean (2 m/day). Each group of data for single standard deviation was utilized to train a single surrogate model. The genetic algorithm-based S-O model was utilized as an efficient optimization solver for such complex optimization task. The results of this study demonstrated that the reliability significantly influenced the design of HWRS. Further, the deterministic optimum design of HWRS was insufficient to be considered a reliable design, especially when a high degree of uncertainty due to the hydraulic conductivity estimation is included. Hence, RBOD for such problem is essential and helps the designer make a decision.

A novel sensitivity analysis model of EANN for F-MWCNTs–Fe[formula omitted]O[formula omitted]/EG nanofluid thermal conductivity: Outputs predicted analytically instead of numerically to more accuracy and less costs

The new approach of “enhanced artificial neural network” (EANN) is developed based on the new generated hybrid nanocomposite of F-MWCNTs–Fe3O4/EG which represents the Functionalized Multi Walled Carbon Nano Tubes together with Fe3O4 nanoparticles, dispersed in ethylene glycol (EG) as the base fluid. Moreover, a new suitable sensitivity analysis is presented which involves a novel proposed method for the sensitivity analysis via ANNs. In this method, the sensitivity of the outputs predicted by means of an ANN to the inputs is calculated analytically rather than numerically. The proposed method not only provides more perceptive, precise and accurate results, but also requires less computational time and cost. The proposed method can be used for all ANNs having various architectures, training algorithms and input–output data sets.

Performance Analysis of Installed Solar PV System Using Homer in Tanzania: A Case Study of Zanzibar and Arusha

This study reflects two photovoltaic (PV) power generations, at Karume Institute of Science and Technology (KIST) - Zanzibar and The Nelson Mandela African Institution of Science and Technology (NM-AIST) - Arusha in Tanzania. The output data sets from each site were verified for possible PV simulation of different operational scenarios to obtain the optimum design configuration. The HOMER software was used to analyze the entire operation of the system. The effect of the accuracy of the photovoltaic integration was determined by analysis of different operational behaviors of the simulated PV levels. Furthermore, the overall performance of the station per site was analyzed for technical, economic and environmental constraints as well as their comparative cost-benefit analysis. The study finds that there is a high sensitivity in the demand for the load (i.e. load growth) whose system performance is characterized with minimum: total net present cost (NPC) of $474,745 and $29,169, feed in tariffs, levelized cost of energy (LCOE) of $1.06/kWh and $0.0118/kWh, total energy output and renewable fraction of 15% and 22% for KIST and NM-AIST respectively, thus support the use of photovoltaic power sources in the generation of energy than their counterpart alternatives because of the best technical performance and is less dependent on other external sources of energy, and simultaneously has good economic and environmental performance

SEMI-DISTRIBUTED NEURAL NETWORK MODELS FOR STREAMFLOW PREDICTION IN A SMALL CATCHMENT PINANG

This paper applied an artificial intelligence methodology for streamflow prediction in a flash flood in Pinang catchment based on TOPMODEL input and output data sets. TOPMODEL is a semi-distributed rainfall runoff model widely used in numerous water resource applications. However, literature has indicated relative weakness in TOPMODEL performances in streamflow prediction. Thus, radial basis function neural network (RBF-NN) has been employed to improve the accuracy of streamflow prediction and then compared with TOPMODEL and multilayer perceptron neural network (MLP-NN) performances. Four years of daily hydro-meteorological data sets (for the period between 2007 to 2010) were used for calibration and validation analysis. The results have shown an improvement from 0.749 and -19.2 of the calibration period to 0.957 and 0.001, and from 0.774 and -19.84 of the validation period to 0.956 and -3.611 of Nash-Sutcliffe model (NS) and Relative Volume Error (RVE), respectively. RBF-NN performance has been established to improve the daily streamflow prediction; however, the MLP-NN was better in contrast with the involved method in the study. It can be concluded that TOPMODEL performance showed a high ability to simulate the peaks compared with both AI methodologies.

Power Quality Improvement in Microgrids Under Critical Disturbances Using an Intelligent Decoupled Control Strategy Based on Battery Energy Storage System

The concept of microgrids (MGs) provides the flexibility to integrate renewables into the power network. Nevertheless, the transience of most renewable energy sources (RESs) exacerbates the power quality of the grid network. Furthermore, the unpredictability of RESs additionally becomes challenging in case of high magnitude disturbances. The deployment and optimal utilization of energy storage systems, to act as an energy buffer are hence pertinent. In this paper, a control strategy for a battery energy storage system (BESS) is formulated based on two intelligent decoupled controllers. The objective is the restoration of system voltage and frequency considering a wide range of disturbances and hence circumvent the power quality degradation. The proposed controller is based on hybrid differential evolution optimization and artificial neural network (DEO-ANN). The controller parameters are tuned online by training the ANN with the sets of input and output data obtained during the process of optimizing the two controllers under low and high disturbances using DEO. Finally, the effectiveness of the proposed controller is validated on a power network consisting of a synchronous generator, photovoltaic power system, and BESS. The simulation results prove the robustness of the proposed control approach as compared with a benchmark controller.

Online Deep Fuzzy Learning for Control of Nonlinear Systems Using Expert Knowledge

This article presents an online learning method for improved control of nonlinear systems by combining deep learning and fuzzy logic. Given the ability of deep learning to generalize knowledge from training samples, the proposed method requires minimum amount of information about the system to be controlled. However, in robotics, particularly in aerial robotics where the operating conditions may vary, online learning is required. In this article, fuzzy logic is preferred to provide supervising feedback to the deep model for adapting to variations in the system dynamics as well as new operational conditions. The learning method is divided into two phases: offline pretraining and online posttraining. In the former, the system is controlled by a conventional controller and a deep fuzzy neural network (DFNN) is pretrained based on the recorded input–output dataset, in order to approximate the inverse dynamical model of the system. In the latter, only the pretrained DFNN is used to control the system. In this phase, the fuzzy logic, which encodes the expert knowledge, is utilized to observe the behavior of the system and to correct the action of DFNN instantaneously. The experimental results show that the proposed online learning-based approach improves the trajectory tracking performance of the unmanned aerial vehicle.

ПРИМЕНЕНИЕ НЕЙРОСЕТЕЙ ДЛЯ ВЫБОРА ИНСТРУМЕНТАЛЬНЫХ СРЕДСТВ ТЕСТИРОВАНИЯ WEB-ПРИЛОЖЕНИЙ НА ПРОНИКНОВЕНИЕ

Penetration testing is conducted to detect and further to fix the security problems of the Web application. During testing, tools are actively used that allows to avoid performing a large number of monotonous operations by the tester. The problem with selecting the tools is that there are a number of similar tools for testing the same class of security problems, and it is not known which tool is most suitable for a particular case. Such a problem is most often found among novice testers, more experienced testers use their own sets of tools to find specific security problems. Such kits are formed during the work, and each tester finds the most suitable tools for him. The goal of the paper is to create a method that will help to choose a tool for a particular case, based on the experience of experts in security testing of Web applications. To achieve the goal, it is proposed to create a Web service that will use the neural net-work to solve the problem of choice. Data for training a neural network in the form of a matrix of tools and their criteria are provided by experts in the field of security testing of Web applications. To find the most suitable tool, a vector of requirements should be formed, i.e. the user of service must specify the criteria for the search. As a result of the search, several most suitable for the request tools are shown to the user. Also, the user can save the result of his choice, if it differs from the proposed one. In this way, a set of learning examples can be extended. It is advisable to have two neural networks, the first one is trained only on data from experts; the second one is trained on data from experts and on data of users who have retained their choice. The usage of neural networks allows to realize correspondence between several input data sets to the one output data set. The described method can be used to select software in various applications.

Fast 3-D Optimization of Magnetic Cores for Loss and Volume Reduction

This paper presents an effective design method for inductors which is based on the multi-objective optimization accelerated by the artificial neural network (ANN). In the learning phase prior to the optimization phase, ANN is trained for 1000 input–output data sets obtained from the finite-element analysis for randomly generated dimensional parameters. The magnetic hysteresis of the ferrite core is modeled by the play model to evaluate the hysteresis losses. The multi-objective optimization problems are solved by the genetic algorithm in which the magnetic loss is effectively computed by the trained ANN to reduce the core volume as well as magnetic loss. The Pareto solutions for an EI-shaped ferrite core are obtained for different inductances. It is shown that the proposed method works much faster than the conventional optimization, and the magnetic loss and the inductance of the optimized inductor agree well with the experimental results.

The MaRIUS‐G2G datasets: Grid‐to‐Grid model estimates of flow and soil moisture for Great Britain using observed and climate model driving data

AbstractThe MaRIUS‐G2G datasets were produced for the MaRIUS (Managing the Risks, Impacts and Uncertainties of drought and water Scarcity) project, using the Grid‐to‐Grid (G2G) national‐scale hydrological model for Great Britain. There are six separate datasets, with each of three combinations of meteorological driving data (two observation‐based and one from climate model ensembles) used to produce two types of outputs (daily time‐series of natural river flow for 260 sites, and monthly 1 × 1 km grids of natural flow and soil moisture in the unsaturated zone). The driving data required by G2G are rainfall and potential evaporation (PE). Two of the datasets from observation‐based driving data use rainfall from CEH‐GEAR (CEH‐Gridded Estimates of Areal Rainfall) with PE from MORECS (Met Office Rainfall and Evaporation Calculation System), and cover the period 1960–2015, while the other two use CEH‐GEAR rainfall with PE derived from 5 km temperature data using the Oudin method, and cover 1891–2015. The two datasets based on driving data (rainfall and PE) from the climate model ensembles cover three periods: Historical Baseline (1900–2006), Near‐Future (2020–2049), and Far‐Future (2070–2099). Data for a 30‐year Baseline period (1975–2004), against which the Near‐Future and Far‐Future periods should be compared, are also available directly. There are 100 members in each ensemble, and the future periods use the RCP8.5 emissions scenario. This paper provides details of the G2G model and the different sets of meteorological driving data, as well as the availability and formatting of the output datasets. It also describes some recent and potential applications of the datasets, which have already been used to support historical and future analyses of low flow and drought characteristics across Britain, and provides some guidance on how the climate model‐driven datasets should (and should not) be used.Open PracticesThis article has earned an Open Data badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at DOI 10.5285/5f3c1a02-d5c4-4faa-9353-e8b68ce2ace2. Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki.

A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks

Recently, deep learning has emerged as a state-of-the-art machine learning technique with promising potential to drive significant breakthroughs in a wide range of research areas. The application of deep learning for network traffic control, however, remains immature due to the difficulty in uniquely characterizing the network traffic features as an appropriate input and output dataset to the learning structures. The network traffic features are anticipated to be even more dynamic and complex in the UDNs of the emerging 5G networks with high traffi c demands coupled with beamforming and massive MIMO technologies. Therefore, it is critical for 5G network operators to carry out radio resource control in an efficient manner instead of adopting the simple conventional F/TDD. This is because the conventional uplink-downlink configuration change in the existing dynamic TDD method, typically used for resource assignment in beamforming and massive-MIMO-based UDNs, is prone to repeated congestion. In this article, we address this issue and discuss how to leverage the deep LSTM learning technique to make localized prediction of the traffic load at the UDN base station (i.e., the eNB). Based on localized prediction, our proposed algorithm executes the appropriate action policy a priori to avoid/alleviate the congestion in an intelligent fashion. Simulation results demonstrate that our proposal outperforms the conventional method in terms of packet loss rate, throughput, and MOS.

A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production

Given that the booming U.S. shale oil has reshaped the global oil landscape, better forecasting output of U.S. shale oil can facilitate to better understanding the global oil trend. To more accurately forecasting U.S. shale oil production, we hybridize the nonlinear- and linear-forecasting model to a new forecasting technique in two step: (i) combining a nonlinear grey model with the mentalism idea to develop nonlinear metabolism grey model (NMGM), (ii) combining the proposed NMGM with Auto Regressive Integrated Moving Average (ARIMA) to develop NMGM-ARIMA technique. The sliding validation experiments with the previous U.S. shale oil quarterly output dataset of 2003–2008 and 2008–2013 are adopted to verify the feasibility of the proposed NMGM-ARIMA technique. The empirical results show that this novel NMGM-ARIMA technique can significantly improve forecasting effectiveness and outperform other related four forecasting models. We therefore believe that the proposed forecasting technique can service researchers, policymaker, analysists and others stakeholders to more timely and accurately forecasting U.S. shale oil output and thus better understanding global oil market.

Automated, strain-based, output-only bridge damage detection

This paper presents a framework for automated damage detection using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge. Stringer–floor beam connection deterioration, a common deficiency, was the focus of this study; however, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The framework utilized Proper Orthogonal Modes (POMs) as damage features and Artificial Neural Networks (ANNs) as an automated approach to infer damage location and intensity from the POMs. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. Input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent, a significant advancement. To develop an automated and efficient output-only damage detection framework, data cleansing and preparation were conducted prior to ANN training. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLABs Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer–floor beam connections, measured signal amplitudes were artificially decreased at select locations. It was concluded that the proposed framework could successfully detect artificial deficiencies imposed on measured signals under operational conditions.

Neural network based redesign of morphing UAV for simultaneous improvement of roll stability and maximum lift/drag ratio

PurposeThe aim of this paper is to redesign of morphing unmanned aerial vehicle (UAV) using neural network for simultaneous improvement of roll stability coefficient and maximum lift/drag ratio.Design/methodology/approachRedesign of a morphing our UAV manufactured in Faculty of Aeronautics and Astronautics, Erciyes University is performed with using artificial intelligence techniques. For this purpose, an objective function based on artificial neural network (ANN) is obtained to get optimum values of roll stability coefficient (Clβ) and maximum lift/drag ratio (Emax). The aim here is to save time and obtain satisfactory errors in the optimization process in which the ANN trained with the selected data is used as the objective function. First, dihedral angle (φ) and taper ratio (λ) are selected as input parameters, C*lβ and Emax are selected as output parameters for ANN. Then, ANN is trained with selected input and output data sets. Training of the ANN is possible by adjusting ANN weights. Here, ANN weights are adjusted with artificial bee colony (ABC) algorithm. After adjusting process, the objective function based on ANN is optimized with ABC algorithm to get better Clβ and Emax, i.e. the ABC algorithm is used for two different purposes.FindingsBy using artificial intelligence methods for redesigning of morphing UAV, the objective function consisting of C*lβ and Emax is maximized.Research limitations/implicationsIt takes quite a long time for Emax data to be obtained realistically by using the computational fluid dynamics approach.Practical implicationsNeural network incorporation with the optimization method idea is beneficial for improving Clβ and Emax. By using this approach, low cost, time saving and practicality in applications are achieved.Social implicationsThis method based on artificial intelligence methods can be useful for better aircraft design and production.Originality/valueIt is creating a novel method in order to redesign of morphing UAV and improving UAV performance.

Model, Validation, and Characterization of a Robust Quantum Random Number Generator Based on Photon Arrival Time Comparison

A Quantum Random Number Generator (QRNG) based on the detection of the arrival time of photons is presented. A stochastic model of the QRNG prototype is derived, showing its consistency with output data sets, in order to estimate the entropy produced by the device. Experimental data, in accord with the model, demonstrates the robustness of the approach versus possible variations of external parameters such as temperature or intensity of the light.

Cecilia: an R package to automate data cleaning of administrative datasets

IntroductionData linkage has considerable potential to improve health and society. Linking vast and detailed information across multiple administrative population-scaled data sources enhances the quality of existing data, empowers population health research, and produces objective evidence to inform policy decisions. In this context, data cleaning is crucial to minimise linkage errors.&#x0D; Objectives and ApproachAs dealing with the heterogeneity of administrative datasets is an acknowledged time-consuming task, the objective is creating a public and open-source R package to automate and report steps of data cleaning in a reproducible fashion.&#x0D; The package automatically assesses variables and reports relevant information and issues for linkage purposes, then cleans the dataset based on problems found, reassesses the variables and reports results again.&#x0D; It has a default cleaning procedure based on years of accumulated linkage knowledge and an interactive exploratory session to check variables individually. The report also includes all settings from both default and interactive session.&#x0D; ResultsThe package accurately detected, cleaned and reported potential linkage problems in variables such as names, addresses and dates in so far 15 actual populational datasets from multiple sources, with a diverse range of format, content, and inconsistencies. The entire process took minutes rather than hours.&#x0D; The reports correctly gathered, organised and presented all relevant information for linkage, in all distinct sections of the hyperlinked document, such as those related to the dataset, individual variables or settings used for cleaning. The different types of information included text, figures, data dictionaries, and frequency tables of detected issues, such as non-alphanumeric characters, annotation terms or suffixes and prefixes.&#x0D; The output datasets had all evaluated variables with cleaned data plus extra columns containing only issues themselves or problematic records.&#x0D; Conclusion/ImplicationsThe package accelerates the data cleaning of linkage variables, automating time-consuming steps, providing pertinent information for linkage as well as cleaned datasets. The complete process is time-efficient and reproducible.&#x0D; As the output dataset contains variables with cleaned data and detected issues, it allows assessment of the level of cleaning performed.

A surrogate-based simulation–optimization approach application to parameters’ identification for the HydroGeoSphere model

A systematic approach is needed to use water more productively, because water shortages limit socio-economic development in many parts of the world. The aim of this paper is to establish a surrogate-based simulation–optimization approach to identify parameter values for a fully integrated surface water and groundwater flow coupling simulation. A surface water and groundwater flow coupling simulation model was implemented using HydroGeoSphere (HGS) model and the parameter sensitivities in the model were analyzed using local sensitivity analysis method. The parameters that exerted a large influence on the output results of the HGS model were then selected as stochastic variables, and the stochastic variable data sets were generated using the latin hypercube sampling (LHS) method, which, thereby, were used as inputs in HGS model to obtain the corresponding outputs. On the basis of input and output data sets, a kriging surrogate model of the HGS model was then established and verified, and parameter values of HGS model were identified using a surrogate-based simulation–optimization approach. The results of this study show that parameters that exert a large influence on the simulation output results include hydraulic conductivity, porosity, the van genuchten parameter ( $$\alpha$$ ), and channel manning coefficient. The established kriging surrogate model is an ideal alternative to the HGS model for simulating and predicting, while optimal parameter values can be identified effectively and accurately using the established approach. The results of this research reveal that huge computational loads can be mitigated while using the kriging surrogate as an alternative for a simulation model in the solution process of optimization model.