Calibration Of Hydrological Models Research Articles

Evaluating the impact of satellite soil moisture data as an additional component in the calibration of a conceptual hydrological model

Abstract This study proposes a new method for dividing a catchment with the aim of testing it in the calibration process of a conceptual hydrological model. The new catchment division is reflected in having different land cover zones and the input data prepared in a semi-distributed way. This study also explores the impact of satellite soil moisture data when multi-objective calibration is used with the land cover zone divisions of a catchment while assigning different weights to runoff ranging from 0% to 100% (with a 0.05 step). The results indicate that using a weight range of 60% to 80% on a runoff provides optimal results, bettering both the runoff model’s efficiency and soil moisture correlation. For further validation of the internal parameters and processes, the field capacity and evapotranspiration of the catchment were monitored. In regions with specially limited in-situ soil moisture data, satellite-derived data can contribute as an scarce additional component of the land cover division that can point out areas of the most reliable soil moisture information.

Optimizing parameter estimation in hydrological models with convolutional neural network guided dynamically dimensioned search approach

Hydrological model calibration plays a crucial role in estimating optimal parameters for accurate simulation. Estimation of parameters is inevitable in hydrological modeling due to the challenge of directly measuring them, as most parameters are conceptual descriptions of physical processes. Modelers commonly employ optimization algorithms for calibrating hydrological models. However, these algorithms often pose computational challenges, especially when dealing with complex physics-based and distributed models. To address these challenges, our study introduces a novel approach called hydroCNN+DDS. By leveraging the strengths of Convolutional Neural Networks (CNN) and the Dynamically Dimensioned Search (DDS) algorithm, hydroCNN+DDS simplifies the model calibration process in complex physics-based models. This approach enables to capture the general patterns and relationships between discharge time series and parameters without compromising the underlying physics. We use hydroCNN+DDS to estimate parameters in the highly parameterized hydrological model, Structure for Unifying Multiple Modeling Alternatives (SUMMA) using hourly observed discharge. Notably, hydroCNN quickly generates sub-optimal parameters, serving as a good initial solution for DDS. This initialization aids DDS in converging faster towards an optimal solution. One of the notable advantages of the hydroCNN+DDS approach is its potential for spatial and temporal transferability. This feature proves valuable in dynamic systems and regions with limited historical data, expanding the applicability of the methodology. Furthermore, our proposed methodology is versatile and can be applied to any simple or complex models, accommodating any variables of interest. The best practices of good model calibration are followed in our approach.

Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model

Large basins with insufficient hydrological station distribution and lacking sufficient meteorological data, such as water level and discharge, pose significant challenges in developing reliable hydrological models. These models are crucial for addressing water resource-related challenges such as climate change (e.g., floods and droughts) and human-induced activities (e.g., dams) impacts. Recent advancements in satellite altimetry offer an efficient alternative by providing water level data; however, integrating this data into hydrological models remains an ongoing challenge. Here we demonstrate that a properly developed hydrodynamic model can effectively translate the satellite-observed water levels at various river cross-sections (hereafter referred to as virtual stations (VSs)) into the required discharge data, facilitating reliable hydrological modeling for data-deficient basins. Our results, based on the assessment of water level data derived from a hydrodynamic model and satellite altimetry, showed high consistency (NSE > 0.96) in the Lancang-Mekong River (LMR) basin as a case study. We found that the distribution of stations along the mainstream, in terms of their number and distance from each other, can significantly impact the performance of hydrological modeling processes. The results revealed that when a large stretch of the river is not monitored by a station, a large uncertainty occurs in runoff generation, at least for that segment of the river. However, the inclusion of virtual stations in the modeling process improved the overall performance, indicating the advantages of satellite altimetry data in better modeling of data-deficient basins. Our findings revealed that virtual stations can be effectively utilized in physically based and spatially distributed hydrological models to develop a sub-basin modeling approach that addresses the regional contribution of sub-basins to downstream river flow. The integrated modeling framework holds potential for hydrological analysis and water resources management for basins lacking sufficient gauging data and provides insight into how the distribution of stations can influence the calibration of hydrological models.

Streamflow diurnal fluctuation and driving mechanism of headwater stream in a semi-humid mountainous region

Diurnal fluctuation in streamflow during rainless periods provides valuable signals for the calibration of hydrological models and comprehension of hydrological processes. Previous research has primarily focused on this phenomenon in the context of single-factor analysis in the Mediterranean, Maritime, and Alpine climates of Middle Europe and Western US. This study is based on the Xitaizi Experimental Watershed (XEW), a typical mountainous headwater watershed situated in the semi-humid monsoon North China Plain. Seven years of high-frequency observation data were used to investigate the characteristics of streamflow diurnal fluctuation, its driving mechanisms, and the propagation pathways of these fluctuation signals through hillslope-riparian-stream. The results are as follows: (1) Streamflow exhibits diurnal fluctuation during rainless periods throughout the year, with variations up to 36% of the daily mean and distinct patterns in rainy (peak at night) and dry seasons (peak in the afternoon). Choosing the proper timing for daily discharge observation can reduce bias due to fluctuations. (2) In rainy seasons, the impact of discharge fluctuation is significant, particularly in years with lower discharge. Meanwhile, the soil moisture in both the hillslope and the riparian zone, and the groundwater level in the riparian zone exhibit a daytime decline and a nighttime rise. The groundwater level in the hillslope fluctuates with a lag time of approximately 6 to 9 hours. (3) Numerical simulations and cross-correlation analysis demonstrate that the dominant driving mechanism of diurnal fluctuations in rainy seasons is the diurnal evapotranspiration variation in the riparian zone, while the influence of aquifer hydraulic conductivity is insignificant.

Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd > TWBM > HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction.

Significance of Multi-Variable Model Calibration in Hydrological Simulations within Data-Scarce River Basins: A Case Study in the Dry-Zone of Sri Lanka

Traditional hydrological model calibration using limitedly available streamflow data often becomes inadequate, particularly in dry climates, as the flow regimes may abruptly vary from arid conditions to devastating floods. Newly available remote-sensing-based datasets can be supplemented to overcome such inadequacies in hydrological simulations. To address this shortcoming, we use multi-variable-based calibration by setting up and calibrating a lumped-hydrological model using observed streamflow and remote-sensing-based soil moisture data from Soil Moisture Active Passive Level 4. The proposed method was piloted at the Maduru Oya River Basin, Sri Lanka, as a proof of concept. The relative contributions from streamflow and soil moisture were assessed and optimised via the Kling–Gupta Efficiency (KGE). The Generalized Reduced Gradient non-linear solver function was used to optimise the Tank Model parameters. The findings revealed satisfactory performance in streamflow simulations under single-variable model validation (KGE of 0.85). Model performances were enhanced by incorporating soil moisture data (KGE of 0.89), highlighting the capability of the proposed multi-variable calibration technique for improving the overall model performance. Further, the findings of this study highlighted the instrumental role of remote sensing data in representing the soil moisture dynamics of the study area and the importance of using multi-variable calibration to ensure robust hydrological simulations of river basins in dry climates.

Monthly Monitoring of Inundated Areas and Water Storage Dynamics in China's Large Reservoirs Using Multisource Remote Sensing

AbstractHigh‐frequency monitoring of reservoir inundation and water storage changes is crucial for reservoir functionality assessment and hydrological model calibration. Although the integration of optical data with synthetic aperture radar (SAR) backscattering coefficients (backscatters) offers an effective approach, conventional methods struggle to consistently provide accurate retrievals over diverse regions and seasons. In this study, we introduce reservoir‐ and monthly‐specific classification models to enhance the integration of Sentinel‐1 SAR backscatters with optical‐based water dynamics. Our method covers 721 reservoirs with a capacity greater than 0.1 km3 in China during 2017–2021. Furthermore, we leverage multisource satellite altimetry records (e.g., ICESat‐2, CryoSat‐2, and GEDI) and digital elevation models to derive hypsometry relationship (i.e., water level–water area relationship) for reservoirs, enabling the transformation of inundated areas into monthly water storage changes for 662 reservoirs, representing 93% of the total storage capacity of large reservoirs. Validation against in‐situ measurements at 80 reservoirs reveals improved monthly inundated area monitoring compared to existing data sets. Additionally, our reservoir water storage change estimates exhibit an average R2 of 0.79 and a mean relative root mean square error (rRMSE) of 21%. Our findings highlight reservoir water increases from May/June to November and declines in winter–spring in most regions. However, the inter‐annual patterns vary among regions, with increases in Northeast China, the Yellow River basin (YR), and Southwest China, contrasted by declines in Eastern and Northwest China. Inter‐ and intra‐annual variability in reservoir water storage is mainly influenced by natural inflow in Northeast and Northwest China, while anthropogenic factors dominate in the YR, Eastern, and Southwest China.

Iber-PEST: Automatic calibration in fully distributed hydrological models based on the 2D shallow water equations

Calibrating physically based hydrological models manually is time-consuming and challenging. Automatic calibration tools have become prevalent in these models; however, its effective use requires not only a deep knowledge of the calibration procedure itself, but also understanding the structure of the model input and output files. In this study, we introduce Iber-PEST, a novel framework combining the parameter estimation and uncertainty analysis package, PEST, with Iber, a 2D model based on the shallow water equations. We demonstrate its capabilities by successfully calibrating eight storm events in northwestern Spain's basins, achieving promising results (mean NSE equal to 0.84). Furthermore, by applying the iterative ensemble smoother included in PEST, we demonstrate the feasibility to use the calibration data generated by Iber-PEST with alternative PEST algorithms not directly integrated in the Iber-PEST framework. This work successfully addresses the significant implementation barrier associated with the automatic calibration of Iber hydrological models with PEST.

Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy

Runoff prediction is a crucial aspect of water resource management and risk mitigation. Despite hydrological modelling plays a vital role in accurately representing catchment behaviour, calibration still poses a significant challenge. This study explores the use of knowable moments (KMoments), a category of high-order statistical moments, as part of the core of objective functions in hydrological model calibration. Traditional objective functions, such as Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE), often make assumptions about data distribution and are sensitive to outliers. KMoments offer a promising alternative by enabling reliable estimation and effective description of high-order statistics from typical hydrological samples and therefore, reducing uncertainty in their estimation and computation of the objective functions in question. Three daily lumped hydrological models (GR4J, VIC, and HYMOD) were employed to test the performance of different calibration strategies using KMoments-based objective functions and compare them with conventional approaches. The hydrological consistency of the simulations was also assessed through 27 hydrological signatures. Our findings highlight the advantages of using KMoments, including improved performance metrics and enhanced hydrological signature reproduction. The findings contribute to advancing hydrological modelling techniques and provide valuable insights for researchers and practitioners seeking to enhance simulation accuracy and reliability.

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Surrogate-Assisted Evolutionary Algorithm for the Calibration of Distributed Hydrological Models Based on Two-Dimensional Shallow Water Equations

Distributed hydrological models based on shallow water equations have gained popularity in recent years for the simulation of storm events, due to their robust and physically based routing of surface runoff through the whole catchment, including hill slopes and water streams. However, significant challenges arise in their calibration due to their relatively high computational cost and the extensive parameter space. This study presents a surrogate-assisted evolutionary algorithm (SA-EA) for the calibration of a distributed hydrological model based on 2D shallow water equations. A surrogate model is used to reduce the computational cost of the calibration process by creating a simulation of the solution space, while an evolutionary algorithm guides the search for suitable parameter sets within the simulated space. The proposed methodology is evaluated in four rainfall events located in the northwest of Spain: one synthetic storm and three real storms in the Mandeo River basin. The results show that the SA-EA accelerates convergence and obtains superior fit values when compared to a conventional global calibration technique, reducing the execution time by up to six times and achieving between 98% and 100% accuracy in identifying behavioral parameter sets after four generations of the SA-EA. The proposed methodology offers an efficient solution for the calibration of complex hydrological models, delivering improved computational efficiency and robust performance.

Getting your money's worth: Testing the value of data for hydrological model calibration

AbstractDespite the big data era, observational data continue to be a limiting factor in the environmental sciences. To collect the most informative field data, studies on the value of data are essential. This article describes a model‐based approach to assess the value of data. While we discuss the approach for hydrological model calibration, the approach is applicable across the environmental sciences. The overall goal is to provide guidance on optimal data collection strategies, that is, what to measure, where, and when.

Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions

AbstractCalibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R2) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46–0.81 and 0.41–0.83, whereas during validation R2 and KGE were found to be in the range of 0.40–0.79 and 0.53–0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region.

Multivariate adaptive regression splines-assisted approximate Bayesian computation for calibration of complex hydrological models

Abstract Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach.

Assessing Recession Constant Sensitivity and Its Interaction with Data Adjustment Parameters in Continuous Hydrological Modeling in Data-Scarce Basins: A Case Study Using the Xinanjiang Model

Data scarcity plays the crucial role in hydrological modeling, causing the uncertainties in hydrological model calibration and parameterization. Therefore, while considering the sensitivity of the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance, especially when there is limited hydro-climatological information. Previous studies have underscored the significance of data adjustment parameter sensitivity and its consequential influence on both Xinanjiang (XAJ) model performance and the determination of the acceptable minimum data length, particularly in data-scarce regions. Nevertheless, it is essential to consider the recession constant sensitivity as it has been identified as the most sensitive parameter on an annual scale while keeping the data adjustment parameters constant during a period of data scarcity. Hence, the objective of this study is to extend the previous research by examining the relationship between recession constant sensitivity and data adjustment parameters in shorter datasets leading to more reliable parameter estimation for data-scarce basins. Five U.S. river basins were analyzed using the 28-year dataset and shorter subsets to highlight the impacts of recession constant sensitivities with different data lengths. This study explores the impact of recession constant sensitivities over the hydrological parameter estimation using two approaches (cg): (i) assessing the relationship between the recession constant (cg) and the data adjustment parameter (Cep), for the 28-year dataset, and (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets, which can affect the estimation of the acceptable minimum data length in the data-scarce basins. The polynomial regression analysis was applied to compare and evaluate the model results, varying over the recession constant with different data lengths. The findings indicated that the influence of the recession constant over the data adjustment parameters in the 28-year dataset is limited in the annual scale. However, there is a significant impact of recession constant sensitivity over the model performance while calibrating the model with subsets, particularly in the worst-case scenario. This study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scarce areas.

Sensitivity analysis in the wavelet domain: a comparison study

Sensitivity analysis plays a pivotal role for the development and calibration of hydrological models, since they are often affected by equifinality. Despite a lot of effort has been placed for the development of effective sensitivity analysis methods, hydrological models remain over parametrized. We take advantage of the evidence that hydrological processes can be described as the superposition of effects occurring at different temporal scales (e.g., seasonal precipitation patterns, seasonal and daily snow and glacier melt, seasonal, daily and sub-daily water management operations) to develop a new framework to perform sensitivity analysis. We apply discrete and continuous wavelet transforms to disentangle hydrological signals occurring at different temporal scales and we take advantage of the different information stored at different temporal scales of the wavelet spectrum to perform a scale-dependent sensitivity analysis. This approach aims to increase the number of identifiable model parameters in comparison to standard sensitivity analysis performed in the time domain. As an exemplary problem, we apply the methodology to synthetic data describing surface water-groundwater interaction in rivers affected by hydropeaking (i.e., sudden fluctuations in the river stage due to hydropower production). The method could be applied also to other models displaying the superposition of processes with different intensities at different temporal scales such as ocean tide propagation in aquifers as well as snow and glacier melt models. The results indicate that considering multiple temporal scales allows us to increase the number of parameters that can be identified and hence calibrated with only a little increase in the computational effort.

A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.

Multi-site calibration of the SWAT hydrological model and study of spatio-temporal variation of water balance components in the Narayani River Basin, central part of Nepal

Abstract Science-policy interaction is vital for addressing hydro-climatic disasters in data-limited regions, with modeling and analysis as key components. The utilization of the soil and water assessment tool (SWAT) model facilitated an evaluation of water balance variations across time and space within Narayani Basin through multi-site calibration. The adjustment of all parameters via the SUFI-2 algorithm revealed that precipitation and temperature lapse rate (PLAPS and TLAPS) exhibit higher sensitivity in scenarios where observed stations fail to capture orographic effects. The calibrated model accurately replicated evapotranspiration, net water yield, and groundwater flow for each sub-basin, including average flow and flow duration curve at calibration points. Findings indicated that 22% of precipitation is lost to evaporation, while 75% contributes to basin runoff, showcasing significant spatial and temporal variability in water balance components. Notably, net water yield comprises 44% lateral flow, 38% surface flow, and 16% groundwater flow, with distinct spatial patterns favoring lateral flow in the Himalayas and groundwater flow in the plains due to topographical variations. These outcomes offer actionable insights for policymakers and water resource managers, enabling assessments of climate and land use impacts and facilitating the formulation of policies for sustainable water resource utilization.

The Strategic Random Search (SRS) – A new global optimizer for calibrating hydrological models

This study introduces a novel global optimization algorithm, Strategic Random Search (SRS), tailored for efficient calibration of hydrological models. SRS outperforms 14 other optimization algorithms on 23 classical benchmark functions and 29 CEC-2017 benchmark functions, demonstrating its superiority on more than half of these tests. Additionally, when applied to rainfall-runoff models, SRS consistently, rapidly, and robustly converges to optimal solutions, surpassing five other algorithms. SRS, developed independently of existing intelligent optimization methods, offers versatility with only two adjustable parameters, making it suitable for various problem types. Through rigorous testing and comparisons, SRS emerges as a robust, widely applicable, and stable convergence algorithm.

Detection of Urban Flood Inundation from Traffic Images Using Deep Learning Methods

Urban hydrological monitoring is essential for analyzing urban hydrology and controlling storm floods. However, runoff monitoring in urban areas, including flood inundation depth, is often inadequate. This inadequacy hampers the calibration of hydrological models and limits their capacity for early flood warning. To address this limitation, this study established a method for evaluating the depth of urban floods using image recognition and deep learning. This method utilizes the object recognition model YOLOv4 to identify submerged objects in images, such as the legs of pedestrians or the exhaust pipes of vehicles. In a dataset of 1,177 flood images, the mean average precision for water depth recognition reached 89.29%. The study also found that the accuracy of flood depth recognition by YOLOv4 is influenced by the type of reference object submerged by the flood; the use of a vehicle as the reference object yielded higher accuracy than using a person. Furthermore, image augmentation with Mosaic technology effectively enhanced the accuracy of recognition. The developed method extracts on-site, real-time, and continuous water depth data from images or video data provided by existing traffic cameras. This system eliminates the need for installing additional water gauges, offering a cost-effective and immediately deployable solution.