Stepwise Calibration Research Articles

Modeling of wave-induced drift based on stepwise parameter calibration

The motion of waves in water causes the slow movement of drifting sea targets—a phenomenon usually ignored in target-drift prediction models for maritime search and rescue (SAR). This study examined the wave-induced drift’s influence on field-observation experiments involving two common, differently sized SAR targets—an offshore fishing vessel (OFV) and a person in the water (PIW)—using parameter stepwise calibration and machine-learning (ML) methods. The sample of wave-induced drift velocity was obtained by gradually separating current-induced (CI) drift’s and wind-induced (WI) drift’s influence from the target-drift velocity using the least-square method and AP98 model. A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. Finally, the drift trajectories considering the influence of waves were fitted and verified based on 2 independent samples respectively. Compared with the force analysis method, the accuracy of the ML methods in the verification test was higher. In addition, the results show that for OFVs, considering wave-induced drift’s influence in the ensemble-trajectory prediction could improve the simulation accuracy. However, for a PIW, no significant improvement was observed. This result also indicates that wave-induced drift may not be simply ignored in large SAR targets’ drift prediction.

An Adaptive Process-Wise Fitting Approach for Hydrological Modeling Based on Streamflow and Remote Sensing Evapotranspiration

Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology.

Bayesian Optimization with Additive Kernels for a Stepwise Calibration of Simulation Models for Cost-Effectiveness Analysis

AbstractA critical aspect of simulation models used in cost-effectiveness analysis lies in accurately representing the natural history of diseases, requiring parameters such as probabilities and disease burden rates. While most of these parameters can be sourced from scientific literature, they often require calibration to align with the model’s expected outcomes. Traditional optimization methods can be time-consuming and computationally expensive, as they often rely on simplistic heuristics that may not ensure feasible solutions. In this study, we explore using Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting structural properties within the solution space. Specifically, we investigate the impact of additive kernel decomposition and a stepwise approach, which capitalizes on the sequential block structure inherent in simulation models. This approach breaks down large optimization problems into smaller ones without compromising solution quality. In some instances, parameters obtained using this methodology may exhibit less error than those derived from naive calibration techniques. We compare this approach with two state-of-the-art high-dimensional Bayesian Optimization techniques: SAASBO and BAxUS. Our findings demonstrate that Bayesian optimization significantly enhances the calibration process, resulting in faster convergence and improved solutions, particularly for larger simulation models. This improvement is most pronounced when combined with a stepwise calibration methodology.

On-orbit high-precision calibration for deep-coupled parameters of star sensor and gyroscope systems.

To enhance the dynamic performance and stability of spacecraft attitude measurement, a commonly adopted approach is to integrate a star sensor with an inertial gyroscope. While an integrated sensor system offers benefits in high-precision and high-dynamic scenarios, it also introduces errors in the extrinsic parameters of the camera and gyroscope, directly contributing to increased fixed error in attitude determination. Hence, real-time correction of integrated system parameters becomes crucial for enhancing the accuracy of attitude estimation. However, a deep coupling relationship exists between the camera's principal point and the extrinsic parameters of the two sensors, unified calibration of system parameters increases the computational time to decouple all the parameters. In response to this challenge, a stepwise calibration method for system parameters is proposed. Firstly, the calibration of camera intrinsic parameters leverages the principle of invariant interior star angles, which are independent of extrinsic parameters. Secondly, based on the observation vector error model of the integrated sensor, observation equations and state equations are constructed, followed by proposing a 4-position maneuver strategy based on observability analysis to achieve rapid decoupling of gyroscope bias, attitude error, and extrinsic parameters. Simulation results affirm the effectiveness of the proposed method, extrinsic parameter errors reaching the arc-second level.

Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance Statement Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.

Mscnet: Mask stepwise calibration network for camouflaged object detection

Mscnet: Mask stepwise calibration network for camouflaged object detection

Stepwise Calibration of Age‐Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

AbstractMany land surface models (LSMs) assume a steady‐state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age‐dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post‐calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates “fast” light‐controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally “slow” growth‐controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age‐dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning‐based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no‐age‐calibrated parameters, our age‐calibrated parameters increased NBP by an average of 50 gC m−2 yr−1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m−2 yr−1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale.

A Stepwise Calibration Method for Successive Start-Up Errors of the Three-Axis Gyroscope Based on the Characteristics of Compass Heading Alignment Error

A Stepwise Calibration Method for Successive Start-Up Errors of the Three-Axis Gyroscope Based on the Characteristics of Compass Heading Alignment Error

Assessment in practice: achieving joint decisions in oral examination grading conversations

ABSTRACT How do examiners reach joint decisions when they grade oral examinations? While government and policymakers provide general frameworks about grading decisions, we know little about how they are actually accomplished in interaction, particularly when examiners initially disagree. We scrutinized 29 video-recorded grading conversations between secondary school examiners using conversation analysis. Results showed that proposing and deciding grades involved a stepwise calibration through which examiners adjusted their individual positions. While in most cases examiners expressed and sought agreement, we also investigate cases where examiners initially disagreed but eventually reached a joint decision. The paper contributes insights into decision-making in institutional interaction, as well as to our understanding of whether and how guidance is implemented in ‘live’ assessment situations. Our findings suggest adjustments are needed both to practice and assessment policy. Data are in Norwegian with English translation.

Data scarce modelling the impact of present and future groundwater development on Jordan multiaquifer groundwater resources

Rapidly growing demands and climate change stresses water resources worldwide and leads to highly competitive situations between the environment and socio-economic development of a region, calling for a smart and modelling driven water resources management. However, data scarcity often prevents the realisation of a comprehensive, nation-wide resources model, which provides reliable and spatially discretized results of water resources development.We present a workflow approach to set up a large-scale multi-aquifer model, overcoming data shortage by stepwise calibration and integrating hydrological and numerical groundwater flow modelling into a coupled system. The study aims to develop such a system to assess how groundwater resources react on anthropogenic impacts on the example of the Kingdom of Jordan, one of the water poorest countries on globe.Simulated heads reliably resembled the monitored ones in >70 % of the observation wells. That makes us confident, the model represents all the states well from 1970, prior to the intense development of the country until 2015. The water balance shows an annual deficit of 1.16 million cubic meter (MCM) due to an overdraft. The discharge to the Dead Sea increased from 564 MCM/yr to 696 MCM/yr over the time period. Regional drawdowns of >250 m and groundwater depression with an extension of approx.100 km are observable in both large aquifer complexes. Most severe areas in the upper calcareous aquifer are located in the north of Amman and practically in all urban and agricultural agglomerations across the country. Groundwater tables in the deeper sandstone aquifer are particularly affected in the south as well as in the wider vicinity of the Dead Sea as consequence of its continuous dropping. Simulations of the future development of the groundwater tables indicate a severe deterioration of the situation with further declines in groundwater levels of up to 70 m.

A New Framework for Reconstructing Time Series DMSP-OLS Nighttime Light Data Using the Improved Stepwise Calibration (ISC) Method

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China’s regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data’s most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields.

Abstract 3362: Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial

Abstract Introduction: Non-small cell lung cancer (NSCLC) response to gefitinib depends on epidermal growth factor receptor (EGFR) mutational status. Response differs according to tumor heterogeneity, notably through clonal selection according to genetic background. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model topredict the effect of EGFR mutations on tumor size in advanced LUAD patients using a mechanistic representation of tumor progression, including response to gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. Methods: 5-step in silico model development: (i) Model Building: Biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. (ii) Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. (iii) Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population.(iv) Validation: A VPOP with comparable baseline characteristics was tested against published patient data.(v) Benchmarking against a Bayesian reference model : (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Results: Our model computed in silico data comparable to the reference model without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. Therefore, the ISELA model has a better percentage of the literature data area contained in the simulated one while the Bayesian model presents a better percentage of the simulated data contained in the literature one. Conclusion: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results with published data, and compared it to an already published model with the same context of use. Both models successfully provide a reliable description of longitudinal tumor size when compared to each other or to observed data. Our model provides a benchmark for future in silico clinical trials. Citation Format: Michael Duruisseaux, Adèle L'Hostis, Emmanuel Peyronnet, Evgueni Jacob, Ben M.W. Illigens, Jim Bosley, Riad Kahoul, Jean-Louis Palgen, Claudio Monteiro. Multiscale EGFR mutated NSCLC tumor heterogeneity knowledge-based model predicts tumor growth under gefitinib: An avenue to in silico clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3362.

Multiscale in silico knowledge-based model predicts tumor growth in advanced EGFR+ NSCLC patients on gefitinib.

e20565 Background: Tumor heterogeneity and treatment resistance remains a continued threat to patients with advanced non-small cell lung cancer (NSCLC), despite the emergence of targeted medicines, e.g. against epidermal growth factor receptor (EGFR) alterations. We developed an in silico EGFR mutated lung adenocarcinoma (EGFR+ LUAD) model to predict the effect of known oncogenic EGFR mutations (common EGFR mutations, EGFR exon 20 insertions). The model provides mechanistic representation of tumor progression, including response to gefitinib and captures tumor heterogeneity, patient age, gender, initial clinical disease stage, and smoking status. Methods: 5-step in silico model development: Model building: biology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from > 300 published papers and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR-downstream-pathways, tumor growth and heterogeneity, gefitinib-PK/PD, treatment-induced resistance and clinical outcome were incorporated into a knowledge-based model containing 27-97 variables, 108-258 parameters and 13-83 ODEs reflecting intra-tumor clonal heterogeneity in a mechanistic manner. Calibration: published spheroid, xenograft and clinical data were used for stepwise calibration to find the correct parameter values. Relevant virtual populations (VPOPs) matching real patients baseline characteristics were generated for model benchmarking and validation. Benchmarking against a published data-based model: coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Validation: a VPOP with comparable baseline characteristics was simulated and results compared against a published patient dataset that wasn’t used in any step during calibration. Matching of prediction over clinical data was done using both coverage and bootstrapped log-rank metrics. Results: Our model provides comparable outputs to the data-based model without having access to the exact original data (our model: precision of 62%, overlap of 91% VS data-based model: precision of 72% and an overlap of 86%). Besides, as a validation criterion, our model also successfully reproduces the time to progression observed in an independent clinical trial (coverage > 99%, negative log-rank tests > 98%). Conclusions: We simulated tumor growth and treatment response in advanced EGFR+ LUAD patients and successfully validated results both against an existing data-based model and a published clinical data. Our model highlights the potential of in silico modeling to better understand complex diseases progression and support efficient development of innovative therapies.

Hydrologic Effects of Fire in a Sub-Alpine Watershed: AgES Outperforms Previous PRMS Simulations

HighlightsAgES and PRMS accurately simulated the dramatic change in streamflow response to precipitation after fire.Measured annual streamflow increased by 170% in 4-year postfire period with similar precipitation.Postfire AgES parameters were drastically reduced for snow and rain interception, canopy density, and ET.Decreased soil depression storage and field capacity also contributed to increased streamflow in AgES.AgES shows promise for simulating hydrologic impacts of sub-alpine forest resource management and fire response.Abstract. Forest fires alter hydrologic responses to precipitation, and streamflow changes can be challenging to simulate. Streamflow data before and after wildfire in a 14 km2 mixed-conifer, sub-alpine watershed in south-central New Mexico were used to calibrate a watershed model (Agricultural Ecosystems Services, AgES) using four years of prefire and postfire conditions. Streamflow results and parameter values from AgES and a prior Precipitation Runoff Modeling System (PRMS) modeling study in the same watershed were compared. Both AgES and PRMS simulated the dramatic change in streamflow response to precipitation after a fire, including a smaller precipitation threshold to induce streamflow. Still, AgES had substantially less bias and better performance when comparing monthly metrics. Only AgES was assessed at the daily scale. AgES simulated daily streamflow accurately throughout the study period, with slight overestimation in the prefire period (Oct 2007 to Oct 2011, 1.2% bias, 0.90 Nash-Sutcliffe efficiency [NSE]) and in the postfire period (Oct 2013 to Oct 2017, 4.4% bias, 0.54 NSE). Although each 4-year model period had nearly identical cumulative precipitation, annual streamflow increased by 170% in the postfire period compared to the prefire period. Appropriate soil and vegetation parameters were modified by the stepwise calibration process to represent the effects of fire in AgES: interception storage was decreased for rain (-99.8%) and snow (-26%), two factors influencing ET were decreased, soil depression storage was reduced (-97%), and soil field capacity was reduced (-50%). AgES demonstrated an improved streamflow simulation compared with PRMS, calibrated parameter shifts that were more consistent to interpret, and particular skill in simulating daily streamflow response to fire. Knowledge gained from this study will guide future simulations of hydrologic responses to fires in the western USA using AgES. Keywords: Agricultural Ecosystems Services model, Forest, Landscape change, Precipitation-Runoff Modeling System, Streamflow change, Surface hydrology, Wildfire.

Simulation of an Oxic-Settling-Anaerobic Pilot Plant Operated under Real Conditions Using the Activated Sludge Model No.2d

Oxic-settling-anaerobic (OSA) process has been introduced into the treatment line of wastewater in order to upgrade activated sludge processes and to reduce the production of excess sludge. The aim of the present study was to simulate the performance of an OSA pilot plant by implementing the Activated Sludge Model No.2d (ASM2d) into a mathematical modelling software (BioWin). The stepwise calibration, performed both by off-line experiments and software dynamic calibration, was carried out in a heuristic way, adjusting the parameters values that showed a major influence to the effluent and internal concentrations. All the reduction factors introduced into ASM2d to simulate the processes occurring in anoxic and anaerobic conditions were lowered in order to reproduce the concentrations of interest. In addition, the values of parameters of the PAOs (polyphosphate accumulating organisms)-related process (namely qPHA and YPO4) were found lower than those usually adopted. In general, theoretical results were in good agreement with the experimental data obtained from plant’s operation, showing an accurate predictive capacity of the model. Good performance was achieved considering the phosphorus removal related process, while some failures were detected in COD and ammonia simulations.

P71.05 Use of a Multiscale NSCLC Tumor Heterogeneity Model to Predict Tumor Growth Under Gefitinib

Novel high-throughput techniques, advanced tumor micro-environment analyses and new insights in tumor cell heterogeneity have significantly increased understanding of regulatory processes in Non-small cell lung cancer (NSCLC): NSCLC treatment response to Gefitinib depends on the epidermal growth factor receptor (EGFR), but differs depending on EGFR gene mutations making clinical prognosis challenging. We therefore developed an in silico EGFR+ lung adenocarcinoma (LUAD) model to predict the effect of EGFR-related mutations on tumor size in advanced-stage adenocarcinoma patients (IIIb or higher), using a mechanistic representation of tumor evolution, including response to Gefitinib. Tumor heterogeneity, age, gender, initial clinical stage, and smoking status are included as covariates. 5-step in silico model development: 1. Model Building: Pathophysiology of EGFR+LUAD was characterized by extracting biological features and their functional relationships from literature and translating them into ordinary differential equations (ODEs). Mutational burden, EGFR downstream-pathways, tumor growth and heterogeneity, Gefitinib-PK/PD, treatment-induced resistance and clinical outcome were modeled in a computational simulation with 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. 2. Calibration: Published spheroid, xenograft and clinical data were used for stepwise calibration. 3. Virtual populations (VPOP): VPOPs were generated for validation and benchmarking respectively, adapting baseline characteristics of a real population. 4. Validation: A VPOP with comparable baseline characteristics was tested against published patient data[1]. 5. Benchmarking against a Bayesian reference model[2]: (1) coverage of experimental interquartile range (IQR) with simulated IQR (precision) assesses model fit with experimental data, (2) coverage of simulated IQR with experimental IQR (overlap) assesses model fit with experimental variability. Our model computed in silico data comparable to the reference model[2] without use of original data for calibration (Figure 1B.2: experimental vs. simulated, precision of 68%, overlap of 91%). The reference model reported precision of 72% and overlap of 86%. We simulated tumor growth and treatment response in advanced-stage adenocarcinoma patients and successfully validated results with a published study[2]. Access to patient-level data for calibration would have improved precision of our model.

P27.06 Multiscale NSCLC Tumor Growth Knowledge-Based Model Reproduces Tumor-Non-Progression under Gefitinib

Non-small cell lung cancer (NSCLC) is the leading form of lung cancer and adenocarcinoma (LUAD) its most common histotype. Tumor size, part of the TNM-staging, is important for prognosis and treatment guidance. Response to Tyrosine Kinase Inhibitors, e.g. Gefitinib, is altered with certain epidermal growth factor receptor (EGFR+) gene mutations making time-to-progression (TTP) predictions difficult. We therefore developed an in silico EGFR+LUAD model to characterize tumor size and TTP in advanced-stage adenocarcinoma patients (IIIb or higher) with EGFR mutations (exon19-deletion (E19+) or exon21-L858R-substitution (E21+)). The model is a mechanistic representation of tumor evolution upon Gefitinib administration, including tumor heterogeneity, age, gender, initial clinical stage and smoking status as covariates. Three-step in silico model development: 1. Model Building using a Knowledge and a Computational Model (CM): Pathophysiology of EGFR+LUAD was characterized with seven sub-models: mutational burden, EGFR downstream pathways, tumor growth and heterogeneity, Gefitinib PK/PD, treatment-induced resistance and clinical outcome. For each sub-model, relevant biological entities and their functional relationships were extracted from scientific papers and translated into ordinary differential equations (ODEs). The CM has 43 variables, 170 parameters and 18 to 83 ODEs reflecting intra-tumor heterogeneity. 2. Calibration with information from scientific literature: Spheroids, xenografts and clinical results were used for stepwise calibration. 3. Validation against published data: Patients[1] (n=159) had E19+ or E21+ mutations and were treated with Gefitinib (250mg/day orally). A Virtual Population with equivalent baseline characteristics was simulated using the calibrated CM. Kaplan-Meier curves show tumor non-progression over time. Validation metrics: (1) “Raw-Data-Coverage” expressing the fit with the 95% prediction interval (PI) of the simulated curve (computed by bootstrapping), (2) Comparison of bootstrapped simulated survival curves (n=159) with log-rank tests (LR) for statistical significance (α=0.05). Coverage of the model simulations with raw data was 95.92%. Proportion of non-significant bootstrapped LR tests comparing the observed curve with simulated curves was 88.43% (Figure 1). We reproduced a clinical trial of advanced-stage EGFR+LUAD patients treated with Gefitinib. We created a modular, reusable, multiscale Knowledge-Based model adaptable to also test the efficacy of other treatments and drug combinations on tumor-non-progression. This enables us to identify best responders and to optimize trial designs in silico.

The Use of Weather Research and Forecasting Model to Predict Rainfall in Tropical Peatland: 1. Model Parameterization

Rainfall dynamics play a vital role in tropical peatland by providing sufficient water to keep peat moist throughout the year. Therefore, information of rainfall data either historical or forecasting data has risen in recent decades especially for an alert system of fire. Here the Weather and Research Forecasting (WRF) model may act as a tool to provide forecasting weather data. This study aims to do parameterization on WRF parameters for peatland in Sumatra, and to perform bias correction on the WRF’s rainfall output with observed data. We performed stepwise calibration to choose the best five physical schemes of WRF for use in the study area. The output WRF’s rainfall was bias corrected by spatially observed rainfall data for 2019 at day resolution. Our results showed the following schemes namely (i) Eta scheme for cloud microphysical parameters; (ii) GD scheme for cumulus cloud parameters, (iii) MYJ scheme for planetary boundary layer parameters; (iv) RRTM for longwave radiation; and (v) New Goddard schemes for shortwave radiation are best combination for being used to predict rainfall in maritime continent. The spatially interpolated observed rainfall with the Inverse Distance Weighting (IDW) was outperformed for calibration process of WRF’s rainfall as shown by statistical indicators used in this study. Further, the findings have contributed to advance knowledge of rainfall forecasting in maritime continent, particularly in providing data to support the development of fire danger rating system for Indonesian peatland.

A parallel computing-based and spatially stepwise strategy for constraining a semi-distributed hydrological model with streamflow observations and satellite-based evapotranspiration

Satellite-based products can provide valuable information for model calibrations and evaluations. However, effectively and efficiently constraining hydrological models with both satellite-based information and streamflow measurements remains a challenge. Here, a parallel computing-based and spatially stepwise strategy that enables separate optimizations of different objective functions in a spatially distributed and parallel manner was proposed for model calibration with streamflow observations and satellite-based evapotranspiration (ET). The new calibration strategy (M5) was tested with the Soil and Water Assessment Tool (SWAT) model in the upper Heihe River basin of China, a mountainous watershed on the northeastern Qinghai-Tibet Plateau. The performance of M5 was evaluated and compared with two single-variable calibration strategies, i.e., streamflow-only calibration and ET-only calibration, and with two multivariate calibration methods, i.e., joint calibration and stepwise calibration. Results indicate that M5 achieves the best model performance among the five calibration strategies in reproducing temporal variations of streamflow and ET. Moreover, M5 improves the simulation of the spatial pattern of ET and attains a higher spatial efficiency than the other calibration strategies. M5 also exhibits a higher computational efficiency, with a magnitude up to 2 times greater than the other calibration strategies, due to the application of parallel computing. Further analysis demonstrates that the new calibration strategy can lead to a synergic relationship between the simulation accuracy of streamflow and ET, underscoring the added value of satellite-based products for model calibrations.

Structural calibration of an semi-distributed hydrological model of the Liard River basin

The development of hydrological models that produce practically useful and physically defensible results is an ongoing challenge in hydrology. This challenge is further compounded in large, spatially variable basins with sparse data, where a detailed understanding of a basin’s hydrological response may be limited. This study presents an iterative and stepwise calibration strategy for model structure and parameters for a hydrological model of the 275,000 km2 Liard River basin in northern Canada. The calibration procedure was optimized to exploit and represent available data at 29 stream gauges and included the use of multiple data sources to constrain model calibration and improve model function. A flexible modelling framework was used to allow the explicit inclusion of locally varied model structure within the calibration procedure. The final model exhibits strong performance in both calibration and validation, and represents significantly different hydrological responses in different portions of the basin well. The calibration procedure helped to identify differences in hydrological processes within the basin which have not been considered by other models of the Liard. The ability to modify model structure in order to account for different hydrological regimes in different parts of the basin is demonstrated to improve model performance locally and globally.