Multi-model Approach Research Articles

Streamflow trends of the Pyrenees using observations and multi-model approach (1980–2013)

Study regionThe Pyrenees. Study focusThe Pyrenees is sensitive to changes in climate (both natural and of anthropic origin) and changes in land use and cover (LULC). These changes can influence the water resources. The historical evolution (1980–2013) of the stream flows are studied using observed time series from non-influenced gauging stations and two models (SASER and SWAT). Their comparison helps to detect and analyze changes in flow rates and their trends (trends are computed using the Sen's slope estimator, the significance of which was evaluated using the Mann-Kendall test). Furthermore, it also allows to explore the question of attribution (these models do not simulate LULC change). New hydrological insights for the regionA complex and diverse domain such as the Pyrenees gives large differences between modelled trends revealing a large uncertainty that has been observed thanks to the use of two models. For the study period, mostly there are no significant trends. When trends are present in the observations and are also simulated, they are attributed to the effects of climate (natural variability and human induced climate change). When the significant trends observed are not simulated by the models, they are mainly attributed to changes in LULC. In general, models have difficulties detecting observed trends, leading to their attribution to changes in LULCs rather than climate, but there are some notable exceptions.

Using a multimodel approach to determine the age and growth of an endangered small shark species in the southern Gulf of Mexico

The bonnethead shark (Sphyrna tiburo), recently upgraded to endangered by the IUCN red list, is frequent in artisanal fisheries’ catches of the southern Gulf of Mexico (GOM). The age and growth parameters were determined from 162 vertebral centra collected from bonnethead sharks (28.2–112.1 cm total length; TL) between 2010 and 2019 in the southern GOM. Two readers determined age estimates using two rounds of independent band pair counts (opaque and translucent). The average percent error (APE) and coefficient of variation (CV) were estimated to assess count precision, and a Bowker test of symmetry was performed to determine potential bias between readers’ counts. The edge and marginal increment analyses were conducted to validate the band pair deposition periodicity, and a multimodel approach was used to select the best growth model. The reading precision was good because APE and CV values were 7.9% and 5.6%, respectively, and the Bowker’s symmetry test indicated no systematic differences between readers. The validation analyses did not provide evidence to determine the band pair deposition periodicity; thus, it was assumed that one band pair is deposited yearly. The oldest male and female were five (95.4 cm TL) and eight (112.1 cm TL) years. The Gompertz growth function provided the best statistical fit and best describes the growth of bonnethead sharks. The parameters were L∞=113 cm, k2=0.49/year, L0=39 cm for females, and L∞=83.6 cm, k2=0.79/year, L0=40.2 cm for males. These are the first age and growth parameters estimated for the bonnethead shark in the southern GOM, which is the second most landed shark and needs a population assessment for proper management.

Global and local drivers of the relative importance of allochthonous and autochthonous energy sources to freshwater food webs

Resource quantity (i.e. organic matter; OM) is a main driver of the prevailing energy pathway in freshwater food webs. The OM pool is mainly composed of allochthonous material, a primary resource for freshwater consumers. Contrastingly, small amounts of autochthonous OM (i.e. algae) can subsidize aquatic communities due to its higher nutritional quality. To date, there is no consensus about the relative importance of allochthonous and autochthonous OM for freshwater food webs or the environmental factors driving their relative importance. We fill this gap by evaluating the relative importance of allochthonous and autochthonous OM sources for freshwater food webs on a global scale through a meta‐analytical approach. We gathered the outcome of stable isotope mixing models of 2789 cases from 58 published studies and calculated a response ratio between the mean contributions of allochthonous and autochthonous OM for freshwater consumers. Using mixed‐effect models and a multimodel inference approach, we tested the influence of latitude, habitat type, ecosystem size, climate and terrestrial productivity over the response ratio. The relative contribution of autochthonous OM was higher in lotic systems. In lentic systems, increasing terrestrial productivity increased the relative contribution of autochthonous OM, while increasing precipitation and temperature seasonality reduced this relative contribution. We suggested that factors increasing terrestrial productivity might also boost autochthonous OM in these systems, while precipitation increases the transport of allochthonous OM to freshwater habitats. We did not find any relationship between environmental factors and the relative contribution of autochthonous OM for lotic systems. We concluded that the relative contribution of allochthonous and autochthonous energy sources to freshwater food webs differs between lotic and lentic ecosystems and it is dependent on multiple environmental factors.

A Discrete Second Order Sliding Mode Multiobserver Based on Multimodel Approach

A Discrete Second Order Sliding Mode Multiobserver Based on Multimodel Approach

Multi-model based soil moisture simulation approach under contrasting weather conditions

We developed and tested a multi-model based soil moisture simulation approach for improving parameterization scheme and its transferability under contrasting weather conditions. Three popular hydrologic models in combination with three optimization schemes were adapted to consider uncertainties in physical and optimization model structures for estimating near-surface soil moisture dynamics. In order to improve parameterization and its transferability under contrasting weather conditions, a multiple set of weights based on different land surface wetness conditions (wet, normal, and dry) were used using a Multiple Weighting Algorithm (MWA). A Differential Split Sample Testing (DSST) scheme was used to test the transferability of parameterizations in the optimization and physical model domains. Data from three experimental sites (Little Washita, LW13 in Oklahoma, Everglades in Florida and Bondville/Olney sites in Illinois) were used to verify our proposed approach. Our findings indicated that the multi-model outputs were highly influenced both by the physical and optimization model structures. Overall, our approach performed well in the test of transferability under different weather conditions. However, we confirmed that the overfitted parameters (to the in-situ measurements during the calibration period) due to the optimization model structures can undermine the transferability of multi-model approach. Also, our findings indicate that the estimated soil hydraulic properties during the dry years can be limited in representing the soil wetness conditions for the wet years.

Control of spacecraft by using multiple model algorithms

Abstract. An effective scheme for fault detection and identification (FDI) and adaptive reconfigurable control (ARC) for controlling a spacecraft under conditions of actuator failures, saturation of control inputs and large external disturbances is proposed. The obtained results increase significantly the autonomy of the spacecraft. The control scheme, based on a general methodology of multiple models, switching and tuning, is designed in the context of nonlinear spacecraft dynamics. The results obtained by using the proposed scheme are illustrated by using numerical simulation algorithms. Keywords spacecraft control, multi-model approach, nonlinear dynamics of spacecraft, numerical simulation algorithm

Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach

Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach

A Multimodel Bayesian Reliability Analysis for a Rock Slope to Counter Data Insufficiency

Data insufficiency of input rock properties is a major issue to analyze the stability of slopes via traditional deterministic and reliability approaches. This data insufficiency in the properties arises due to complexities associated with in-situ and lab testing of rocks. The traditional Bayesian approach overcomes this issue by considering uncertainties in model parameters by combining available prior information neglecting the uncertainty associated with the distribution type/probability model. This study proposes a novel Bayesian multimodel inference approach to incorporate the uncertainties associated with probability models/distribution types along with model parameters for rock properties. The approach first identifies a set of candidate probability models and then employs the Bayesian framework to incorporate the parameter uncertainties for each model. The approach is demonstrated for a rock slope case with the potential of structurally controlled planar failure. It is concluded that the approach effectively treats the statistical uncertainties associated with probability model types and parameters with limited data and provides a more realistic stability assessment than the traditional Bayesian approach. Results show that the uncertainty in probability model parameters affects the stability of rock slope much more significantly than model types.

The Boy Who Cried Bear: Multi-Model Approach for Predicting Market State

The Boy Who Cried Bear: Multi-Model Approach for Predicting Market State

Математические и графовые модели специализированных мультиагентных систем с распределенным решением прикладных задач

The concept of a specialized multi-agent system (SMAS) as a system designed for a distributed solution of either one applied problem or a highly limited range of applied problems is considered. Such problems frequently require a large number of homogeneous operations, which can lead to a significant increase in the size of the system and, as a result, to inefficient control and a drop in performance. Therefore, it is advisable to divide a large-sized open SMAS into classes of agents and find relationships between them. This corresponds to dividing the applied task T into subtasks and determining relations between these subtasks. Such a path entails a two-level distributed control over SMAS agents. At the first and second levels, respectively, the relations of agents within classes and between classes of agents are studied. This paper proposes a multi-model approach to constructing, modeling, and analyzing SMAS. The properties of the agent and its mathematical model, described by a 5-digit set, as well as the mathematical model of the class of agents, described by a 12-digit set, which includes such components as the collective knowledge base of agents, the collective communication environment, mental, social, and other components, are discussed. Relations between classes of agents are reflected in a mathematical model of open SMAS. If a partition of a problem T is represented by an undirected acyclic graph GR′ whose terminal vertices have degree one and whose interior vertices have degree two, then GR′ is defined as a simple tree. In the case when any class of agents is a complete graph К then the Cartesian product CP′ = GR′ × Kn, according to the proved theorem, is a graph model of an open SMAS, consisting of the components К and connectives between them. According to the corollary of this theorem, it is possible to pass from CP′ to the multigraph model MYL′ = (MV′, ME′) of the system, if the components К are associated with the vertices of this model mv′ com  MV′, and the connectives are associated with its edges me′ con1  ME′. Moreover, the number of edges between any two vertices MYL′ is equal to n. The analysis of multigraph models of open SMAS of large sizes will significantly reduce the complexity of their study at the structural level.

Fault detection using sliding mode multiobserver for nonlinear systems: Validation on a real chemical process

The design of an accurate observer is still a challenging problem for nonlinear systems subject to fault signals. This paper addresses the problem of simultaneous state and sensor fault estimation for discrete-time nonlinear systems. An uncoupled multimodel approach is adopted to deal with nonlinear systems subject to sensor faults. A simple sensor fault dynamic is exploited to transform this fault signal into a pseudo-actuator fault scenario. A new sliding mode multiobserver is proposed for the estimation of sensor faults and system states. This multiobserver is computed as a fusion of a set of local sliding mode observers with respective activation functions. The asymptotic stability of the proposed multiobserver is formulated in terms of linear matrix inequalities using the Lyapunov theory. A simulation example is retained to demonstrate the effectiveness of the proposed strategy. As well, a real-time application on a transesterification reactor is carried out to validate the theoretical results.

Interacting multiple model-based ETUKF for efficient state estimation of connected vehicles with V2V communication

Accurate prediction of the motion state of the connected vehicles, especially the preceding vehicle (PV), would effectively improve the decision-making and path planning of intelligent vehicles. The evolution of vehicle-to-vehicle (V2V) communication technology makes it possible to exchange data between vehicles. However, since V2V communication has a transmission interval, which will result in the host vehicle not receiving information from the PV within the time interval. Furthermore, V2V communication is a time-triggered system that may occupy more communication bandwidth than required. On the other hand, traditional estimation methods of the PV state based on individual models are usually not applicable to a wide range of driving conditions. To address these issues, an event-triggered unscented Kalman filter (ETUKF) is first employed to estimate the PV state to strike a balance between estimation accuracy and communication cost. Then, an interactive multi-model (IMM) approach is combined with ETUKF to form IMMETUKF to further improve the estimation accuracy and applicability. Finally, simulation experiments under different driving conditions are implemented to verify the effectiveness of IMMETUKF. The test results indicated that the IMMETUKF has high estimation accuracy even when the communication rate is reduced to 14.84% and the proposed algorithm is highly adaptable to different driving conditions.

Prediction of elevated groundwater fluoride across India using multi-model approach: insights on the influence of geologic and environmental factors.

Elevated fluoride in groundwater is a severe problem in India due to its extensive occurrence and detrimental health impacts on the large population that thrives on groundwater. Although fluoride is primarily a geogenic pollutant, existing model-based studies lack the amalgamation of the influence of geologic factors, specifically tectonics, for identifying groundwater fluoride distribution. This drawback encourages the present study to investigate the association of the tectonic framework with fluoride in a multi-model approach. We have applied three machine learning models (random forest, boosted regression tree, and logistic regression) to predict elevated groundwater fluoride based on fluoride measurements across India. The random forest model outperformed other models with an accuracy of 93%. Tectonics was found to be one of the most important predictors alongside "depth to water table." Two major areas of high risk identified were the northwest parts and the south-southeast cratonic peninsular region. The random forest model also performed significantly well over the validation dataset. We estimate that nearly 257 million people are exposed to elevated fluoride risk in India. We endeavor that the findings of our study would be an effective tool for identifying the areas at risk of elevated fluoride and also assist in undertaking effective groundwater management strategies.

A multi-model approach to explore sustainable food and land use pathways for Argentina

A multi-model approach to explore sustainable food and land use pathways for Argentina

Hybrid Approaches-Based Sliding-Mode Control for pH Process Control.

This paper presents two hybrid control topologies; the topologies are designed by combining artificial intelligence approaches and sliding-mode control methodology. The first topology mixes the learning algorithm for multivariable data analysis (LAMDA) approach with sliding-mode control. The second offers a Takagi-Sugeno multimodel approach, internal model, and sliding-mode control. The process under study is a nonlinear pH neutralization process with high nonlinearities and time-varying parameters. The pH process is simulated for multiple reference changes, disturbance rejection, and noise in the transmitter. Performance indices are used to compare the proposed approaches quantitatively. The hybrid control topologies enhance the performance and robustness of the pH process under study.

Genetic, Epigenetic and Environmental Factors Influence the Phenotype of Tooth Number, Size and Shape: Anterior Maxillary Supernumeraries and the Morphology of Mandibular Incisors

The aim of this study is to investigate whether the genetic, epigenetic and environmental factors that give rise to supernumeraries in the maxillary incisor region and larger dimensions of the adjacent maxillary incisors are also associated with variations in the morphology of the mandibular incisors. If so, this would contribute to understanding the distribution and interactions of factors during dental development and how these can be modelled. The sample consisted of 34 patients with supernumerary teeth in the maxillary anterior region, matched for gender, age and White Caucasian ethnicity with 34 control subjects. The average ages of the supernumerary and control groups were 12.8 and 12.2 years, respectively. Study models of all subjects were constructed and imaged using a previously validated system. Using custom software, each of the mandibular incisor teeth were measured to obtain 17 parameters from the labial view and 17 from the occlusal view. Principal component analysis (PCA) was used to summarize the measurements into a smaller set representing distinct features of the clinical crowns, followed by a comparison between the supernumerary and control groups using 2-way ANOVA. Seven factors of tooth size of the mandibular central incisors and six factors of the mandibular lateral incisors were identified as major features of the clinical crowns. All parameters of both mandibular incisors were greater in the supernumerary group than in the control, with three of these, located in the incisal and cervical regions of the mandibular lateral incisors, being statistically significantly larger. The findings of this study indicate that the aetiological factors associated with supernumerary teeth in the maxillary anterior region also affect tooth crown dimensions of mandibular incisors. This new evidence enhances several models of the interactions of genetic, epigenetic and environmental components of dental development and supports a multi-model approach to increase understanding of this process and its variations.

Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

Age and growth of the Pacific hake, Merluccius productus (Actinopterygii: Gadiformes: Merlucciidae), in the Gulf of California: A multimodel approach

Over the past decade, the fishery of Pacific hake, Merluccius productus (Ayres, 1855), has increased in the Gulf of California, Mexico; therefore, any biological–fisheries information is highly relevant for the management of this fishery, and information on age and individual growth would be fundamental to evaluate populations. The objective of the presently study was to assess age, based on otolith structure, and estimate growth parameters through a multimodel approach. Specimens were collected during research cruises of BIP XI from 2014 to 2019. Pacific hake from the Gulf of California ranged in length from 12.5 to 105 cm TL, reaching a maximum age of 13 years, and females were four years older than males. The logistic model was the best model to describe age–size data for both sexes. Females reached 50% of the maximum length at five years old and males reached that length at four years old.

The Effects of Landscape Heterogeneity on East China Anuran Communities: Identifying Spatial Scales in an Urbanizing Landscape

Cities are growing rapidly worldwide, with over half of the human population living in cities. Amphibians are the most threatened vertebrates on our planet and are particularly vulnerable to the effects of urbanization. While it is known that landscape features and scales are important for amphibians in urban areas, we do not adequately understand how the urban landscape affects diversity patterns, nor have we identified which spatial scale is most appropriate for evaluating how amphibians respond to urban environments. In this study, we examined the relationships between anuran abundance/richness and landscape features at four spatial scales in Shanghai, China. In order to determine the relative importance of landscape variables and the most appropriate spatial scale, a multi-model inference approach was used to evaluate and compare model weighted mean coefficients. Our results show that large spatial scales, i.e., 1500 m and 2000 m, best predicted relative anuran abundance and richness, while the total anuran abundance responded most strongly to landscape variables at smaller scales, i.e., 500 m and 1000 m. Patch richness and the interspersion and juxtaposition index play a large role in predicting the anuran species’ richness and abundance. The abundance of P. nigromaculatus, F. multistriata, and B. gargarizans increased with patch richness. Species richness and total abundance were most strongly related to the interspersion and juxtaposition index. Our research highlights the importance of identifying the most suitable spatial scale in urban environments because not all anuran respond to the same spatial scale. We found that the relationships between anuran relative abundance and species and urban habitat features are not consistent with the prediction of other landscapes (e.g., farmland, forest, and island). Additionally, constructing diverse habitat patches and more neighboring habitats may maintain or improve anuran communities in urbanizing landscapes.

Rural electrification subsidy estimation: a spatial model development and case study

A global push to achieve universal electricity access, paired with drastic reductions in the cost of decentralized electricity technologies, has led to significant research on how best to roll out access to rural communities in sub-Saharan Africa. Various geospatial electrification models have been developed to aid the decision-making process considering decentralized grid alternatives such as mini-grids and solar home systems. Despite these tools suggesting that in many cases, decentralized systems are a more cost-effective electricity access pathway, grid extension still predominates in practice. This is due, at least in part, to institutional structures in most countries that provide significant direct and indirect subsidies to grid extension projects, commonly through publicly-owned utilities. These sources of finance are generally not available to primarily privately operated off-grid energy service providers. However, the subsidy provided for grid extension projects is not well understood. In this paper, we employ utility grid extension costs and revenue data, and geospatial grid infrastructure data to estimate the size and distribution of subsidy implicitly provided to rural grid extension projects for 129 communities in Mombasa County, Kenya. We also estimate subsidies for hypothetical off-grid electricity systems in the same communities that would deliver equivalent services to the grid. We allocate the cost of shared medium voltage (MV) distribution infrastructure using a marginal and an average cost method for grid extension and compare these with subsidies for off-grid systems. We find that the average of average subsidy per customer across communities for grid extension is US$5,118 and US$5,330 for the two MV cost allocation methods respectively, while for the off-grid systems the corresponding average of average subsidies are US$3,380, using a real discount rate of 1.3% evaluated from a nominal discount rate of 8% and inflation rate of 6.7%. Our results show that in the communities in our case study, 40% and 37% of the communities would command less subsidy while served by minigrids over the grid, and the switch would save 50% and 54% of the total cost for average and marginal cost allocation methods respectively. We also show that by using a multi-model approach to electrification and by reallocation of implicit subsidies that have been exclusive to grid extension to other technology options utilities can cast the net wider, without an increase in budgets.