Data-based Mechanistic Modelling Research Articles

Emotional States versus Mental Heart Rate Component Monitored via Wearables

Psychiatric illnesses are estimated to account for over 15% of the burden of disease, which is more than all kinds of cancer together. Since mental disease is often preceded by issues in emotion processing, a method to objectively measure emotions in daily life would be needed. The goal of this research is to investigate the possibilities of mental heart rate component, assessed with a real-time individualized algorithm that decomposes total heart rate in a physical, basal, and mental component, to classify discrete emotions. For this aim, twenty participants committed to wearing a wristband 24/7 for three months and to label the occurrence of fourteen emotions on their smartphones. In total, 1255 labels were added. The dynamics of the mental heart rate component responses to emotions were identified via data-based mechanistic transfer function models. For the classification, the numerator and denominator model orders and parameters, the four features that define transfer function models, were used as features in a support vector machine classifier. This resulted in an average classification accuracy of the mental heart rate responses of 51.1% over all participants, compared to a random classifier with an average accuracy of 28.5%. We concluded that the dynamics of emotions are not only highly variable between individuals, but that they are also time varying on an individual basis. To increase accuracy, more and higher quality labels are indispensable.

Modeling streamflow in headwater catchments: A data-based mechanistic grounded framework

Study RegionThe Lenguazaque River Basin is a 290 km2 headwater catchment of the Fuquene Lake Watershed in the Colombian Andes. Regional stakeholders are optimizing water allocations for all users, given numerous quantity related issues such as its simultaneous use for conservation, agriculture, and coal mining. Study FocusDeveloping hydrological models in headwater catchments is challenging, especially in developing countries where technical resources and data are limited. This study addresses these challenges by proposing strategies to develop reliable yet mathematically simple models, requiring fewer inputs than data-intensive alternatives. Three stages are proposed, focused on 1) assessing hydrological data, 2) preparing datasets for modeling, and 3) developing models in daily and sub-daily resolutions. The last stage is grounded on a data-based mechanistic modeling approach and includes a novel combination of baseflow separation with digital filters and multi-objective optimization principles. Having a hydrophysical meaning, obtained models are suitable for simulating alternative scenarios. New hydrological insights for the regionResulting daily models achieved an acceptable performance, significantly better than a semi-distributed model. The performance of sub-daily models was sub-optimal, yet it can be improved by enhancing the quality of sub-daily datasets and the efficiency of computational algorithms. Due to their mathematical nature, the proposed strategies are expected to be applicable to other headwater catchments where improving water management is an imperative need.

A New Framework Based on Data-Based Mechanistic Model and Forgetting Mechanism for Flood Forecast

A New Framework Based on Data-Based Mechanistic Model and Forgetting Mechanism for Flood Forecast

Study of pollution transport through the rivers using aggregated dead zone and hybrid cells in series models

This paper aims at studying the data-based mechanistic pollution transport models of the aggregated dead zone (ADZ) and hybrid cells in series (HCIS) models. For a better understanding of them, it was tried to connect their parameters to the physical parameters of the classical advection–dispersion (ADE) model. A series of experiments were conducted using the laboratory model of the gravel bed river, and the sodium chloride solution was chosen as a conservative tracer. A new analytical solution of HCIS model, including triple dispersion cells, was developed. Also, the theoretical temporal moment equations were derived and attempted to develop explicit relationships for both ADZ and HCIS parameters based on hydraulically and geometrically variables. Detailed descriptions of the Pe number and the skewness coefficients (CSK) were presented, and the theoretical limit of CSK was extracted. It was concluded that the range of (0.93–1.24) for CSK is acceptable, which confirms the finding of previous investigators. A sensitivity analysis was operated to find the importance of different variables in estimating model parameters. Finally, the theoretical breakthrough curves of ADZ and HCIS models were drawn and confirmed with experimental BTCs. Statistical goodness of fit parameters of RMSE, DC, and MAE was calculated as triple sets of (0.0158, 0.99, 21.58 ppm), (0.0188, 0.97, 27.16 ppm), and (0.105, 0.83, 78.84 ppm) for ADZ, HCIS, and ADE models, respectively, which showed the superiority of data-based models to the classical ADE model.

Real-time modelling of indoor particulate matter concentration in poultry houses using broiler activity and ventilation rate

Measuring particulate matter concentration in poultry houses remains as a difficult task, primarily because aerosol analysers are expensive, require specialist knowledge to operate and are labour intensive to maintain. However, it is well known that high concentrations of particulate matter causes health and welfare problems with livestock, farm workers and people living in the vicinity of the farm premises. In this work, a data-based mechanistic model is developed to relate broiler activity and ventilation rate with indoor particulate matter concentration. For six complete growing cycles, in a U.K. commercial poultry farm, broiler activity was monitored using a camera-based flock monitoring system (eYeNamic®) and ventilation rate was measured. Indoor particulate matter concentration was continuously monitored by measuring size-segregated mass fraction concentrations with the aerosol analyser DustTrak™. A discrete-time multi-input single-output time-invariant parameters Transfer Function model was developed to determine the particulate dynamics within each day of the growing cycle in the poultry house using broiler activity and ventilation rate as inputs. This model monitored indoor particulate matter concentration with an average accuracy of R T 2 = ( 51 ± 26 ) % . A dynamic linear regression modelling with time-variant parameters improved average accuracy with R T 2 = ( 97.7 ± 1.3 ) % . It forecasted one sample-ahead the indoor particulate matter concentration level, using a time window of 14 samples, with a mean relative prediction error, M R P E = ( 4.6 ± 3.2 ) % . Thus, dynamic modelling with time-variant parameters has the potential to be part of a control system to manage in real-time indoor particulate matter concentration in broiler houses.

Computational Fluid Dynamics and Data–Based Mechanistic Modelling of a Forced Ventilation Chamber

The research behind this article ultimately concerns control system robustness and optimisation for the regulation of temperatures in multiple buildings that are linked to a controllable external heating supply network. Lancaster University campus is being used as a case study, for which the building management system provides data. Nonetheless, situations arise when it is difficult or expensive to obtain suitable data for specific rooms or buildings and, in such cases, computational fluid dynamics (CFD) models are utilised to investigate relevant heat transfer phenomena. Such models can be limited by their complexity and they are inappropriate for model-based control design. Hence, the present article investigates a hybrid approach based on both CFD and data-based mechanistic (DBM) models. DBM models are obtained initially from statistical analysis of observational time-series but are only considered credible if they can be interpreted in physically meaningful terms. A laboratory forced ventilation chamber is used to investigate the modelling issues arising and to make recommendations relating to the wider project. The chamber is first discretised into finite volumes and the associated Navier-Stokes equations are solved to determine the physical properties of each zone. The model responses are compared with experimental data and analysed using the DBM approach.

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Abstract. Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15 h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10–50 km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.

Commentary on “Data-based mechanistic modelling and forecasting globally averaged surface temperature”

The daily consumer price index (CPI) produced by the Billion Prices Project (BPP CPI) offers a glimpse of the direction taken by consumer price inflation in real time. This is in contrast to the official U.S. CPI, which is compiled monthly and released with an average of a three-week delay following the end of the reference month. A recent body of research contended that the movements of online prices are representative of those of offline retail prices, making the BPP CPI a natural candidate for accurately improving the timeliness of the official CPI. We assess the predictive content of the BPP CPI using a variety of MIDAS models that accommodate data sampled at different frequencies. These models generate estimates that remain robust to the variety of time periods considered and, by the standard of the existing literature, contribute to a significant upgrade in the forecast accuracy of official consumer price inflation figures. The paper then sketches the broad implications of BPP CPI for the consumer price statistics maintained by national statistics offices and discusses how the proposed improvement in the timeliness of the official CPI fits in this perspective.

Extended Data-Based Mechanistic Method for Improving Leaf Area Index Time Series Estimation with Satellite Data

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32.

System identification of the upper part of Murray River

Data based modelling is an important tool for obtaining models of rivers. In this paper we consider several identification methods, namely Prediction Error Method, Maximum Likelihood, continuous time system identification, Refined Instrumental Variable method (used within the context of Data-Based Mechanistic modelling) and Subspace Identification Method, and apply them to real data from the Murray River in Australia. Both Multiple-Input Single-Output models where the output is the water level in a lake and Multiple-Input, Multiple-Output models where in addition a flow is also modelled, are considered. The models are compared in terms of their accuracy on validation data and on how easily the methods can incorporate prior knowledge.

Data-based mechanistic modeling approach for predicting thermal response of conductive food during heating processes

The application of a data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was completely filled in 307x113 steel cans as the conductive food. Step increase in heating medium was applied while the product temperature was recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. A first order transfer function model proved to be sufficiently good in describing the heat transfer from heating medium to product with a high statistical significance (R2 > 0.99). In this model, a parameter related to the heat transfer coefficient was found and could be used to predicting of product temperature during heating processes.

Data-based mechanistic modeling approach for predicting thermal response of conductive food during heating processes

The application of a data based mechanistic modeling approach to predict thermal histories of conductive foodstuffs during heating is reported. In the experiment, minced fish was completely filled in 307x113 steel cans as the conductive food. Step increase in heating medium was applied while the product temperature was recorded. The simplified refined instrument variable algorithm was used as model parameter identification tool to obtain the best model order and parameters. A first order transfer function model proved to be sufficiently good in describing the heat transfer from heating medium to product with a high statistical significance (R2 > 0.99). In this model, a parameter related to the heat transfer coefficient was found and could be used to predicting of product temperature during heating processes.

Environmental Modelling: Finding Simplicity in Complexity

Preface. List of Figures. List of Tables. Introduction (John Wainwright and Mark Mulligan). Part I: Modelling and Model Building. 1 Modelling and Model Building (Mark Mulligan and John Wainwright ). Part II: The State of the Art in Environmental Modelling. 2 Climate and Climate-System Modelling (L. D. Danny Harvey). 3 Soil and Hillslope Hydrology (Andrew Baird). 4 ModellingCatchment Hydrology (Mark Mulligan). 5 Modelling Fluvial Processes and Interactions (Katerina Michaelides and John Wainwright). 6 Modelling the Ecology of Plants (Colin P. Osbourne). 7 Spatial Population Models for Animals (George L. W. Perry and Nick R. Bond). 8 Ecosystem Modelling: Vegetation and Disturbance (Stefano Mazzoleni, Francisco Rego, Francesco Giannino and Colin Legg). 9 Erosion and Sediment Transport (John N. Quinton). 10 Modelling Slope Instability (Andrew Collison and James Griffiths). 11 Finding simplicity in complexity in biogeochemical modelling (Harald Sverdrup and Hordur Haraldsson). 12 Modelling human decision making (John Wainwright and Mark Mulligan). 13 Modelling land-use change (Eric F. Lambin). Part III: Models for Management. 14 Models in Policy Formulation and Assessment: The WADBOS decision support system (Guy Engelen). 15 Decision Support Systems for Managing Water Resources (Sophia Burke). 16 Soil Erosion and Conservation (Mark A. Nearing). 17 Modelling in Forest Management (Mark J. Twery). 18 Stability and Instability in the Management of Mediterranean Desertification (John B. Thornes). Part IV: Current and Future Developments. 19 Scaling Issues in Environmental Modelling (Xiaoyang Zhang, Nick A. Drake and John Wainwright). 20 Environmental Applications of Computational Fluid Dynamics (Nigel G. Wright and Chris J. Baker). 21 Self-Organization and Cellular Automata Models (David Favis-Mortlock). 22 Data-Based Mechanistic Modelling and the Simplification of Environmental Systems (Peter C. Young, Arun Chotai and Keith J. Beven). 23 Pointers for the Future (John Wainwright and Mark Mulligan).

Evaluation of climate change impacts on streamflow to a multiple reservoir system using a data-based mechanistic model

Recent investigations have demonstrated scientists' consensus on the increase in global mean temperature and climate variability. These changes alter the hydro-climatic condition of regions. Investigation of surface water changes is an important issue in water resources planning as well as for the operation of reservoirs. In this study a data-based mechanistic (DBM) model has been used for daily streamflow simulation. This model is a data-driven statistical base simulation model that can take advantage of additional climate variables with time variable configurations. The model has been developed for simulation of streamflow to three reservoirs, located in central Iran, using the daily rainfall, temperature and streamflow data. Comparison of the DBM results with the autoregressive integrated moving average model, as an alternative model, shows its higher performance. To include climate change impacts in study, an artificial neural network-based statistical downscaling model is developed for rainfall and temperature downscaling. The downscaled temperature and rainfall data under climate change scenarios based on HadCM3 general circulation model outputs are used to evaluate the climate change impacts on streamflow for the 2000–2050 time horizon. The results demonstrate the considerable impact of climate change on streamflow variability with significantly different behaviour in the three adjacent basins.

Model-based cell number quantification using online single-oxygen sensor data for tissue engineering perfusion bioreactors.

Online and non-invasive quantification of critical tissue engineering (TE) construct quality attributes in TE bioreactors is indispensable for the cost-effective up-scaling and automation of cellular construct manufacturing. However, appropriate monitoring techniques for cellular constructs in bioreactors are still lacking. This study presents a generic and robust approach to determine cell number and metabolic activity of cell-based TE constructs in perfusion bioreactors based on single oxygen sensor data in dynamic perfusion conditions. A data-based mechanistic modeling technique was used that is able to correlate the number of cells within the scaffold (R(2) = 0.80) and the metabolic activity of the cells (R(2) = 0.82) to the dynamics of the oxygen response to step changes in the perfusion rate. This generic non-destructive measurement technique is effective for a large range of cells, from as low as 1.0 × 10(5) cells to potentially multiple millions of cells, and can open-up new possibilities for effective bioprocess monitoring.

Data-based mechanistic modelling and validation for leaf area index estimation using multi-angular remote-sensing observation time series

Spatially and temporally complete leaf area index (LAI) time series are required for crop growth monitoring, forest biomass estimation, and land surface process simulation studies. Global LAI products currently available include the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. However, data quality still needs to be improved, especially with respect to temporal continuity. In this research, a new approach has been developed to estimate LAI time series using the data-based mechanistic (DBM) modelling procedure. Both the nadir viewing reflectance and anisotropic index (ANIX) time series derived from the MODIS product are used in LAI_DBM modelling and estimation, where the ANIX values are used as an auxiliary variable to represent the bidirectional reflectance anisotropy of the vegetation canopy. Both the MOD09GA multi-angular remote-sensing observations and the MOD15A2 LAI products are used in the LAI time series modelling and retrieval procedure. Ground measurements at typical vegetation sites are used to validate the estimated LAI. The preliminary results show that: (1) the new LAI_DBM approach using nadir viewing reflectance observation and ANIX time series can be used to improve the continuity of estimated LAI time series. The disturbance noise introduced by using the MOD09A1 directional reflectance observations directly can thus be reduced. (2) An ANIX time series can represent the vegetation canopy bidirectional reflectance anisotropy information and its dynamic changes. It works well in the retrieval procedure for improving LAI time series estimation. (3) The preliminary retrieval results demonstrate that the estimated LAIs can achieve better time series continuity than the original MODIS LAI product.

Streamflow and hydrogen ion interrelationships identified using data-based mechanistic modelling of high frequency observations through contiguous storms

With the aim of quantifying the purely hydrological control on fast water quality dynamics, a modelling approach was used to identify the structure (and dynamic response characteristics or DRCs) of the relationship between rainfall and hydrogen ion (H+) load, with reference to rainfall to streamflow response. Unlike most hydrochemistry studies, the method used makes no a priori assumptions about the complexity of the dynamics (e.g., number of flow-paths), but instead uses objective statistical methods to define these (together with uncertainty analysis). The robust models identified are based on continuous-time transfer functions and demonstrate high simulation efficiency with a constrained uncertainty allowing hydrological interpretation of dominant flow-paths and behaviour of H+ load in four upland headwaters. Identified models demonstrated that the short-term dynamics in H+ concentration were closely associated with the streamflow response, suggesting a dominant hydrological control. The second-order structure identified for the rainfall to streamflow response was also seen as the optimal model for rainfall to H+ load, even given the very dynamic concentration response, possibly indicating the same two flow-paths being responsible for both integrated responses.

Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts

Data-based mechanistic (DBM) models can offer a parsimonious representation of catchment dynamics. They have been shown to provide reliable accurate flood forecasts in many hydrological situations. In this work, the DBM methodology is applied to forecast flash floods in a small Alpine catchment. Compared to previous DBM modelling studies, the catchment response is rapid. The use of novel radar-derived ensemble quantitative precipitation forecasts based on analogues to drive the DBM model allows the forecast horizon to be increased to a level useful for emergency response. The characterization of the predictive uncertainty in the resulting hydrological forecasts is discussed and a framework for its representation illustrated.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Smith, P.J., Panziera, L., and Beven, K.J., 2013. Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts. Hydrological Sciences Journal, 59 (7), 1343–1357. http://dx.doi.org/10.1080/02626667.2013.842647

Environmental modelling: finding simplicity in complexity

Preface. List of Figures. List of Tables. Introduction (John Wainwright and Mark Mulligan). Part I: Modelling and Model Building. 1 Modelling and Model Building (Mark Mulligan and John Wainwright ). Part II: The State of the Art in Environmental Modelling. 2 Climate and Climate-System Modelling (L. D. Danny Harvey). 3 Soil and Hillslope Hydrology (Andrew Baird). 4 ModellingCatchment Hydrology (Mark Mulligan). 5 Modelling Fluvial Processes and Interactions (Katerina Michaelides and John Wainwright). 6 Modelling the Ecology of Plants (Colin P. Osbourne). 7 Spatial Population Models for Animals (George L. W. Perry and Nick R. Bond). 8 Ecosystem Modelling: Vegetation and Disturbance (Stefano Mazzoleni, Francisco Rego, Francesco Giannino and Colin Legg). 9 Erosion and Sediment Transport (John N. Quinton). 10 Modelling Slope Instability (Andrew Collison and James Griffiths). 11 Finding simplicity in complexity in biogeochemical modelling (Harald Sverdrup and Hordur Haraldsson). 12 Modelling human decision making (John Wainwright and Mark Mulligan). 13 Modelling land-use change (Eric F. Lambin). Part III: Models for Management. 14 Models in Policy Formulation and Assessment: The WADBOS decision support system (Guy Engelen). 15 Decision Support Systems for Managing Water Resources (Sophia Burke). 16 Soil Erosion and Conservation (Mark A. Nearing). 17 Modelling in Forest Management (Mark J. Twery). 18 Stability and Instability in the Management of Mediterranean Desertification (John B. Thornes). Part IV: Current and Future Developments. 19 Scaling Issues in Environmental Modelling (Xiaoyang Zhang, Nick A. Drake and John Wainwright). 20 Environmental Applications of Computational Fluid Dynamics (Nigel G. Wright and Chris J. Baker). 21 Self-Organization and Cellular Automata Models (David Favis-Mortlock). 22 Data-Based Mechanistic Modelling and the Simplification of Environmental Systems (Peter C. Young, Arun Chotai and Keith J. Beven). 23 Pointers for the Future (John Wainwright and Mark Mulligan).