Model Output Research Articles

Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models

Accurate spatiotemporal flood simulations are essential for making informed decisions regarding flood release in affected regions, such as flood detention areas. Traditional spatiotemporal flood simulation approach uses partial differential equation (PDE) models (or physics-based models), which need high computational time. Although many machine learning (ML) models for inundation are increasingly being used to emulate the PDE models to address this issue, utilizing conventional ML models to achieve large-scale spatiotemporal flood prediction (i.e., simulation output in tens of thousands of grids and time steps over the whole flood event) remains a significant challenge. Therefore, we developed a new inundation model (IM) using encoder-decoder long short-term memory (ED-LSTM) with Time Distributed Spatial output model (ED-LSTM-TDS) that can acquire accurate spatially distributed flood information more rapidly. In the new IM framework, each ED-LSTM-TDS is built to simultaneously generate output for multiple (K) cell grids and multiple ED-LSTM-TDS models are built for prediction at all grids of entire PDE model. This study is the first of its kind to employ the ED-LSTM-TDS method to address spatiotemporal flood inundation simulation problems for flood detention areas. A 1994 km2 flood detention area in northeastern China was used as a case study. ED-LSTM-TDS exhibited better performance in predicting flood characteristics (e.g., water depth, velocity) than alternative methods, including ordinary LSTM, artificial neural network (ANN), and multiple linear regression (MLR). In addition, we investigated the trade-off relationship between the accuracy of flood characteristic prediction and the computation time of the proposed model by considering different numbers of K cell grids in each ED-LSTM-TDS model. The final proposed inundation model could accurately predict the spatiotemporal flood characteristics within 1.5 min to acquire the same information that required approximately 1 h by the PDE models. Such rapid and accurate prediction by the proposed model is important for evacuation planning, upstream flood control decisions and flood damage reduction.

Multi-attribute decision-making method of pumped storage capacity planning considering wind power uncertainty

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness.

Global supply chains amplify economic costs of future extreme heat risk

Evidence shows a continuing increase in the frequency and severity of global heatwaves1,2, raising concerns about the future impacts of climate change and the associated socioeconomic costs3,4. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

Systematic review of the uncertainty of coral reef futures under climate change

Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world’s coral reefs. This prognosis is primarily based on a small subset of available models that apply similar ‘excess heat’ threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. ‘Excess heat’ models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field’s ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.

Electric Motor Chain Modeling Using Artificial Neural Networks and Semi-Empirical Methods

The methodology presented in this paper is dedicated to a user who wants to design a power chain of an electric unmanned aerial vehicle. The power chain includes a brushless direct current (BLDC) motor, a propeller, and a battery. Three models are presented to predict the thrust, electrical consumption, mechanical torque, and rotational speed provided by each component of an electrical power chain. Only public data were used to design these models. The research was developed in order to be used by all people who want to buy electrical power chain components and therefore to model their properties. Neural networks set with MATLAB parameters were used to design BLDC motor output (rotational speed and mechanical torque) models. Empirical methods such as blade element theory (BET) and disk area were used to model propeller thrust. A battery discharge model was designed based on public datasheets. Very good prediction results were obtained for the rotational speed (0.20% of error) and torque (0.45% of error). Small thrust modeling errors of 1.06% were found with the BET method. Models presented in this research could be designed using a wide type of data, such as public data (provided on manufacturer websites) or real test bench measurements.

CO2 terms of trade and its determinants based on input–output models with technical differences

This paper constructs a comparative analysis framework on how the input-output (IO) model with technical differences affects the calculation of the pollution terms of trade (PTT) and the tests of the pollution haven hypothesis. Specifically, the CO2 terms of trade (CTT) of the world's major economies are calculated based on five IO models, and chain additive structure decomposition analysis (SDA) is conducted to examine the roles of different factors in the changes in CTT. The economic phenomena reflected by the CTT measured by these IO models are found to be different, and a comparative analysis shows that different IO models are suitable for studying different economic problems. Suggestions are provided on the application of different IO models in the calculation of economic indicators and the study of economic issues. Policy makers need to be cautious about policy recommendations based on the results obtained from different IO models.

Multi-objective calibration and uncertainty analysis for the event-based modelling of flash floods

ABSTRACT This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events.

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment.

Care for patients with heart failure (HF) causes a substantial load on health care systems where a prominent challenge is the elevated rate of readmissions within 30 days following initial discharge. Clinical professionals face high levels of uncertainty and subjectivity in the decision-making process on the optimal timing of discharge. Unwanted hospital stays generate costs and cause stress to patients and potentially have an impact on care outcomes. Recent studies have aimed to mitigate the uncertainty by developing and testing risk assessment tools and predictive models to identify patients at risk of readmission, often using novel methods such as machine learning (ML). This study aims to investigate how a developed clinical decision support (CDS) tool alters the decision-making processes of health care professionals in the specific context of discharging patients with HF, and if so, in which ways. Additionally, the aim is to capture the experiences of health care practitioners as they engage with the system's outputs to analyze usability aspects and obtain insights related to future implementation. A quasi-experimental design with randomized crossover assessment will be conducted with health care professionals on HF patients' scenarios in a region located in the South of Sweden. In total, 12 physicians and nurses will be randomized into control and test groups. The groups shall be provided with 20 scenarios of purposefully sampled patients. The clinicians will be asked to take decisions on the next action regarding a patient. The test group will be provided with the 10 scenarios containing patient data from electronic health records and an outcome from an ML-based CDS model on the risk level for readmission of the same patients. The control group will have 10 other scenarios without the CDS model output and containing only the patients' data from electronic medical records. The groups will switch roles for the next 10 scenarios. This study will collect data through interviews and observations. The key outcome measures are decision consistency, decision quality, work efficiency, perceived benefits of using the CDS model, reliability, validity, and confidence in the CDS model outcome, integrability in the routine workflow, ease of use, and intention to use. This study will be carried out in collaboration with Cambio Healthcare Systems. The project is part of the Center for Applied Intelligent Systems Research Health research profile, funded by the Knowledge Foundation (2021-2028). Ethical approval for this study was granted by the Swedish ethical review authority (2022-07287-02). The recruitment process of the clinicians and the patient scenario selection will start in September 2023 and last till March 2024. This study protocol will contribute to the development of future formative evaluation studies to test ML models with clinical professionals. PRR1-10.2196/52744.

The shipping industry under the EU Green Deal: An Input-Output impact analysis

In 2021, the European Commission introduced the ‘Fit for 55’ package, a set of policies aimed at reducing greenhouse gas (GHG) emissions by at least 55% by the year 2030. This initiative, aligned with the European Green Deal, seeks to make Europe a climate-neutral continent by 2050. A pivotal aspect of ‘Fit for 55’ is the proposed extension of the European Union Emissions Trading System (EU ETS) to the shipping industry. According to the European Commission, water transport accounts for 2.5% of global GHG emissions, while the shipping industry accounts for 13% of the EU’s GHG emissions from transportation.This stringent new shipping policy makes it imperative to determine how the world’s economy will respond, considering three distinct implementation proposals with different rates of policy introduction. Evaluating the short-term impacts of the policy is crucial to ensuring that the sustainability initiative is not only ambitious but also pragmatic and adaptable to the realities. To assess the implications, this paper uses the Leontief price model, the environmental input–output (EIO) model, and the OECD Inter-Country Input-Output (ICIO) Table for 2018, which contains information for 45 sectors. The focus is on EU countries and its Top 10 Trading Countries.Our findings show that a short-term trade-off exists between economic and environmental goals and that environmental gains incur economic losses for key stakeholders. They also show that the impact of this policy is felt more by producers in the EU and consumers in non-EU countries than by other agents in the respective countries. Lastly, a recommendation from our study is that the policy should be phased in progressively to provide economic agents with the necessary adjustment time and thus minimize economic losses.

Centralized vs. Decentralized Electric Grid Resilience Analysis Using Leontief’s Input–Output Model

Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid’s resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid’s resilience, this study employs an extrapolation of Leontief’s input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids.

Improved BIO-Based Chinese Automatic Abstract-Generation Model

With its unique information-filtering function, text summarization technology has become a significant aspect of search engines and question-and-answer systems. However, existing models that include the copy mechanism often lack the ability to extract important fragments, resulting in generated content that suffers from thematic deviation and insufficient generalization. Specifically, Chinese automatic summarization using traditional generation methods often loses semantics because of its reliance on word lists. To address these issues, we proposed the novel BioCopy mechanism for the summarization task. By training the tags of predictive words and reducing the probability distribution range on the glossary, we enhanced the ability to generate continuous segments, which effectively solves the above problems. Additionally, we applied reinforced canonicality to the inputs to obtain better model results, making the model share the sub-network weight parameters and sparsing the model output to reduce the search space for model prediction. To further improve the model’s performance, we calculated the bilingual evaluation understudy (BLEU) score on the English dataset CNN/DailyMail to filter the thresholds and reduce the difficulty of word separation and the dependence of the output on the word list. We fully fine-tuned the model using the LCSTS dataset for the Chinese summarization task and conducted small-sample experiments using the CSL dataset. We also conducted ablation experiments on the Chinese dataset. The experimental results demonstrate that the optimized model can learn the semantic representation of the original text better than other models and performs well with small sample sizes.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Assessing Future Hydrological Variability in a Semi-Arid Mediterranean Basin: Soil and Water Assessment Tool Model Projections under Shared Socioeconomic Pathways Climate Scenarios

Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.

Surgical Modulation of Pulmonary Artery Shear Stress: A Patient-Specific CFD Analysis of the Norwood Procedure.

This study created 3D CFD models of the Norwood procedure for hypoplastic left heart syndrome (HLHS) using standard angiography and echocardiogram data to investigate the impact of shunt characteristics on pulmonary artery (PA) hemodynamics. Leveraging routine clinical data offers advantages such as availability and cost-effectiveness without subjecting patients to additional invasive procedures. Patient-specific geometries of the intrathoracic arteries of two Norwood patients were generated from biplane cineangiograms. "Virtual surgery" was then performed to simulate the hemodynamics of alternative PA shunt configurations, including shunt type (modified Blalock-Thomas-Taussig shunt (mBTTS) vs. right ventricle-to-pulmonary artery shunt (RVPAS)), shunt diameter, and pulmonary artery anastomosis angle. Left-right pulmonary flow differential, Qp/Qs, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI) were evaluated. There was strong agreement between clinically measured data and CFD model output throughout the patient-specific models. Geometries with a RVPAS tended toward more balanced left-right pulmonary flow, lower Qp/Qs, and greater TAWSS and OSI than models with a mBTTS. For both shunt types, larger shunts resulted in a higher Qp/Qs and higher TAWSS, with minimal effect on OSI. Low TAWSS areas correlated with regions of low flow and changing the PA-shunt anastomosis angle to face toward low TAWSS regions increased TAWSS. Excellent correlation between clinically measured and CFD model data shows that 3D CFD models of HLHS Norwood can be developed using standard angiography and echocardiographic data. The CFD analysis also revealed consistent changes in PA TAWSS, flow differential, and OSI as a function of shunt characteristics.

Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking

The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input–output model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton’s law or Lagrange’s equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input–output model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input–output model and the sliding mode control when new experimental data become available.

Timeseries partitioning of ecosystem respiration components in seasonal, non-tropical forests; comparing literature derived coefficients with evaluation at two contrasting UK forest sites

IntroductionUnderstanding carbon flows within ecosystems is key to quantifying the impacts of land-use change in the climate. However, while the net exchange of CO2 between the ecosystem and atmosphere indicates global warming potentials, partitioning into individual flux components is needed to understand sinks and sources, residence times, and sensitivities to land-use impacts. Scaling from research site to region requires modelling evaluated against in situ measurements, but there is often a mismatch between outputs of process models (e.g., soil heterotrophic respiration (Rh)) and site-measured parameters (e.g., total soil surface respiration (Rs) or whole ecosystem respiration (Re)).MethodsThis study took a literature review approach to determine fractional coefficients for estimating Rh from Re or Rs and considered whether these fractions differed across a year in seasonal forests, where relative contributions of root respiration might be expected to vary between growing and dormant seasons. Compiled timeseries data were grouped by forest type (broadleaf, needleleaf, and mixed), and coefficients for a fraction of each component (Rs or Re) that Rh represented were calculated using two approaches, namely a simple annual mean value over all months and individual monthly means. These coefficients were then used to estimate Rh separately from higher-level fluxes (Re from eddy covariance and Rs from soil chambers), measured concurrently at two UK forest sites, and compared to Rh estimated from the same datasets using previously published generic coefficients as well as to concurrently measured Rh and Re.ResultsBoth approaches resulted in much closer convergence of the two separate estimates of Rh (derived from Re or Rs) than previously published coefficients, particularly for Rh/Re coefficients that had previously been measured under peatland blanket bog rather than forest.Discussion/ConclusionThis result suggests that land cover is an important factor in determining the relative contribution of heterotrophic respiration to higher-level fluxes and that the coefficients used would ideally be derived from studies on similar ecosystems.

Analysis of the evolution and drivers of carbon inequality based on a human well-being equity perspective

Regional development imbalances lead to the geographic separation of producers and consumers in interregional trade, thus resulting in an unequal distribution of carbon emissions (CE) and human well-being (HWB). Few studies have analyzed the long-term dynamic evolution and driving factors of interregional carbon inequality from the perspective of HWB equity. This study calculates the changes in embodied CE and HWB and their interregional flows and changes in carbon inequality in 2007, 2012, and 2017 using a multiregional input‒output (MRIO) model, and analyzes the drivers of interregional CE/HWB transfer, and then makes policy implications. The results indicate that carbon inequality in China shows a worsening trend because of the mismatch between the change in CE transfers in carbon-intensive sectors and the change in HWB transfers in economic and social capital. At the national level, CE intensity, HWB intensity, production structure, and per capita consumption have the most significant effects on embodied CE and embodied HWB. The direction of the factor drivers affecting interregional net carbon emissions (NCE) and net human well-being (NWB) transfer depends largely on the resource endowment and the extent of the effects of the different drivers and development status of two regions. This study provides a new perspective for analyzing the evolution of carbon inequality in China and provides important insights for reducing carbon inequality and realizing carbon equity.

Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

A Comprehensive Analysis of China’s Water Resources Tax Reform

In response to the growing disparity between the supply and demand of water resources, the Chinese government has piloted a more binding and reformed water resources tax to realize the sustainable utilization of water resources. However, reasonable tax standards and their water-saving effects and economic benefits are important and worthy of attention. Therefore, in this study, we combine the virtual water theory with the price input–output model to discuss the impact of water resources taxation on the economy and its water-saving effects. The results show the following: (1) A water resources tax has a significant water-saving effect, and is predicted to save 33.12 billion cubic meters of virtual water. (2) Consumers’ expected reductions in spending on food and tobacco manufacturing and agriculture are predicted to save more virtual water at a lower economic cost. (3) The collection of water resources taxes can actively and simultaneously guide water savings in terms of consumption and production. The water consumption of the construction industry is worthy of attention. The expected output value reduction accounts for 67.2% of the total output value reduction, and its water savings account for 96% of the total water savings. Other service sectors also have ample room to optimize the utilization of their water resources.

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.