Capacity Model Research Articles

Pollutant Load Capacity of Rawa Besar Lake, Depok, West Java

Depok City has dozens of lakes and one of them is Lake Rawa Besar. The Depok government gives priority to Rawa Besar Lake to be development as a tourist destination. At this time the waters of Rawa Besar Lake are in a polluted condition caused by domestic waste, land use change, chicken farming and floating net caramba. The study aims to analyze water quality, determine the carrying capacity of pollutant loads and provide recommendations for pollutant load reduction. The calculation of the Pollution Load Capacity refers to Minister of Environment Regulation No. 28 of 2009. Based on water quality analysis, 5 parameters exceed the quality standard, such as BOD, COD, total phosphate, total nitrogen, and total coliform. Using the model and calculation of the pollutant load capacity of lake and/or reservoir. The pollutant load capacity of Lake Rawa Besar for BOD parameters is 50.26 kg/year while the existing load is 262.76 kg/year, COD is 418.81 kg/year existing load is 1150.41 kg/year, phosphate is 0.50 kg/year existing load 26.45 kg/year, nitrogen 12.56 kg/year existing load 85.88 kg/year and total coliform 8.4 x 104 amount/year existing load 9.6x106 amount/year. The burden of incoming pollutants exceeds the pollutant load capacity of Lake Rawa Besar. Pollution control efforts are carried out by implementing communal wastewater treatment systems such as an Anaerobic Baffled Reactor (ABR).

Analytical Model for the In-Plane Lateral Capacity of Unreinforced Masonry Walls Based on Effective Compression Zone Failure Mechanism

ABSTRACT This study proposes a simplified theoretical model to predict the in-plane lateral capacity of unreinforced masonry (URM) walls, addressing limitations in current design guidelines based on semi-empirical approaches. Masonry walls are conceptualized as macroscale units subjected to vertical and lateral loads, with failure mechanisms governed by the effective compression zone. The model applicability is evaluated against comprehensive URM datasets and compared with existing guidelines. The results indicated the outperformance of the proposed model compared to existing guidelines. Additionally, a parametric study explores the influence of primary design parameters on the in-plane lateral capacities of URM walls, offering valuable predictive insights.

REVIEW AND COMPARATIVE STUDY OF HYDROLOGICAL MODELS FOR RAINFALL-RUNOFF MODELLING

Water is considered as an important resources for human existence on the earth. In order to simulate or optimized hydrological data for various water resources management, several hydrological models are very useful to attain this aim for water resources management and as a decision support tools. A rainfall-runoff model is a quantitative prototype explaining the rainfall-runoff interactions at basin scale. The hydrological models have peculiarities in terms of capabilities for various water resources management. This paper tends to reviewed over fifty (50) papers that are peculiar to hydrological models as applicable to rainfall-runoff modeling. It involved evaluating and comparing different hydrological models used in simulating rainfall process converting into surface runoff for water use efficiency. Several runoff models such as Hydrologic Engineering Center - Hydrologic Modeling System (HEC-HMS), Soil and Water Assessment Tool (SWAT), Precipitation-Runoff Modeling System (PRMS), Variable Infiltration Capacity model (VIC), LISt-based Erosion Model (LISEM), MIKE Surface Water - Groundwater Hydrology (MIKE SHE) and Runoff Prophet were critically assessed. Rainfall-runoff models are globally utilized for different applications to enhance water use efficiency across different sectors. However, types of hydrological models by examining various hydrological models, model accuracy by evaluating the accuracy and reliability of each model in predicting runoff from rainfall data, scope of applications by determining the adequacy of the models for numerous geographical regions and climatic circumstances, complexity and usability by assessing the complexity of the models, their data requirements, ease of use and computational efficiency, also the models advantages and limitations in capturing the dynamics of the rainfall-runoff process were critically assessed. This was to aid modeling objectives. It was inferred that HEC-HMS is widely applied for modelling precipitation-runoff processes in watersheds of various sizes, aiding in flood forecasting, reservoir operation, and water management for agricultural and urban water use efficiency. SWAT is used for assessing the impact of land management practices (e.g., crop rotation, irrigation, land use changes) on water resources, including runoff generation and water quality, thus optimizing water use efficiency in agriculture. PRMS is applied to model the transport of water via complex hydrological systems, aiding in watershed management and water use efficiency assessments. In conclusion, this comparative review seeks to guide water scientists, the users of hydrological models and hydrological engineers in selecting the most suitable models for their specific modelling needs for sustainable water resources management.

Influence of bolt preload of anchors on bond-slip behavior of fiber fabric-steel interface

Bond-slip behavior is a common failure mode in steel structures reinforced with fiber fabric. Premature debonding of the fiber fabric significantly impacts the effectiveness of steel structure reinforcement. In this study, a gripping anchor is proposed to enhance the reliability of the interface between fiber fabric and steel plates. Experimental investigations were conducted to analyze the influence of anchor bolt preload on the double shear joint between fiber fabric and steel plates. 41 specimens were prepared and tested to explore the effects of parameters such as bolt preload, anchor position, number and type of fiber fabric layers, and bond length on load-bearing capacity. Comparative analyses were conducted on failure modes, ultimate loads, and stress-strain distributions of the specimens. Experimental results indicate that the position of the anchor has a minimal impact on the ultimate bearing capacity of the specimen. However, anchors positioned closer to the joint end exhibit lower ductility upon failure. A novel predictive model was developed to characterize the bond-slip behavior of preload applied to carbon fiber fabric and steel plates. The theoretical model of the ultimate bearing capacity in the yield stage fits well with the experimental results of specimens with three layers of carbon fiber fabric, with a difference range of 0.34 % to 14.97 % compared to actual results. This indicates that the model has high accuracy in predicting stress-strain and bearing capacity.

Driving digital transformation and business model innovation in tourism through innovation labs: An empirical study

In the rapidly changing digital landscape, Innovation Labs have become pivotal agents of transformation, yet their application in tourism remains underexplored. This study addresses this gap through multiple case studies and an action research project focused on tourism. The findings reveal that Innovation Labs significantly boost digital innovation capacity, digital transformation, and business model innovation within tourism organizations. The proposed management framework offers practical guidance for effective implementation, emphasizing their potential to democratize technology and innovation, particularly for SMEs. These conclusions underscore the role of Innovation Labs as key drivers of innovation, providing valuable insights for both academics and practitioners.

Implementing collaborative pre-task planning with intermediate Arab EFL learners in a blended, task-based environment: A mixed methods study

Pre-task planning (PTP) has received scant attention in L2 writing and task-based research, especially pertaining to its effect on the writing production of intermediate, school L2 learners. This study examines mainly the influence of implementing collaborative PTP on the writing production of Arab EFL learners within blended, task-based environment. Following a mixed methods experimental design, intermediate L2 learners were distributed in planning and no-planning conditions and conducted a pre- and post-test. Students in both groups composed a number of argumentative essays over a 10-week period, which were analyzed for complexity, accuracy, fluency, ideational content, and lexical richness. Results from independent samples t-test and univariant analysis of covariance (ANCOVA) indicated several significant differences between participants' performance on the post-test compared to the pre-test but with differential effects in terms of the five measures. Analyses revealed that PTP didn't promote all the measures simultaneously, but it mainly assisted L2 writers produce enhanced content planning (idea generation, lexis, and argumentation) and resulted in greater fluency and overall improved organization and writing quality. Results of classroom observations supported the statistical results and showed that blended, task-based PTP positively impacted students' writing performance, motivation, self-confidence, and learning independence, and thus created a positive, engaging L2 writing environment. The study results were interpreted through the lens of cognitive processing theories, particularly Skehan's (1998, 2014) Limited Attentional Capacity Model and Robinson's (2001) Cognition Hypothesis, providing support to Robinson's position that PTP led to greater fluency. The use of data and methodological triangulation in this study offers new insights into the role of PTP in the L2 writing process, especially with intermediate, school learners in an authentic classroom setting, and thus contributes to the inconsistent results of previous PTP studies (Ellis, 2021).

Shear performance and capacity of FRP reinforced concrete beams: Comprehensive review and design evaluation

To enhance the durability of concrete structures in harsh environments, replacing steel bars with FRP bars is judged a feasible method. The low elastic modulus and transverse strength of FRP bars result in the weak shear capacity of concrete beams reinforced with FRP bars. Reviewing and summarizing existing literature are important for addressing this challenge. The research progress on the shear behaviour of concrete beams reinforced with FRP bars was systematically described from two aspects in this paper. In the aspect of literature review, shear mechanisms and main influencing factors of concrete and FRP stirrups were summarized. In addition, achievements and shortcomings of existing literature were summarized, and potential research directions were pointed out. In the aspect of design method evaluation, a database containing 525 samples was established and calculation models of shear capacity of concrete beams reinforced with FRP bars in seven codes were evaluated. Evaluation parameters mainly included shear span ratio, effective height, and maximum strain of FRP stirrups. Based on the calculation model in Italian code CNR DT203-06, an optimized model was proposed to improve the accuracy in predicting concrete shear contribution. The review and evaluation in this paper had important reference significance for improving the design level of concrete structures reinforced with FRP bars and promoting the engineering application of FRP bars.

Regression-classification ensemble machine learning model for loading capacity and bucking mode prediction of cold-formed steel built-up I-section columns

This paper presents a regression-classification ensemble machine learning (ML) model for loading capacity and, in particular, buckling mode prediction with respect to cold-formed steel (CFS) I-section columns. The ML model comprises two sub-models, including the regression sub-model for load capacity prediction and the classification sub-model for buckling mode prediction. A total of 541 experimental and numerical data for CFS built-up I-section columns with varies geometric dimensions, material properties and buckling modes were collected from the literature to construct a dataset. To improve the accuracy and the explainability of the ML model on relative small-scale dataset, physical information-enhanced features based on the knowledge of the direct strength method were adopted as the input features of the ML model, including the yield strength, the elastic local buckling load, the elastic distortional buckling load and the elastic global buckling load, instead of directly using the parameters of geometric dimensions and material properties as the input features. The appropriateness of seven classical machine learning algorithms was compared, namely Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Adaptive Boosting (ADA), Extreme Gradient Boosting (XG), and Categorical Boosting (CB). The statistical analysis showed that the XG algorithm had the highest level of accuracy for both loading capacity and buckling mode prediction. In addition, the explainability analysis indicated that the developed ML model successfully learned the mechanical characteristic of CFS built-up I-section columns. Finally, the developed ML model was further compared with the existing codified method in the literature. The comparison results indicated that the developed ML model can significantly improve the level of accuracy for loading capacity and buckling mode prediction, which provides a promising alternative choice for the design of CFS built-up I-section columns

CENN: Capsule-enhanced neural network with innovative metrics for robust speech emotion recognition

Speech emotion recognition (SER) plays a pivotal role in enhancing Human-computer interaction (HCI) systems. This paper introduces a groundbreaking Capsule-enhanced neural network (CENN) that significantly advances the state of SER through a robust and reproducible deep learning framework. The CENN architecture seamlessly integrates advanced components, including Multi-head attention (MHA), residual module, and capsule module, which collectively enhance the model's capacity to capture both global and local features essential for precise emotion classification. A key contribution of this work is the development of a comprehensive reproducibility framework, featuring novel metrics: General learning reproducibility (GLR) and Correct learning reproducibility (CLR). These metrics, alongside their fractional and perfect variants, offer a multi-dimensional evaluation of the model's consistency and correctness across multiple executions, thereby ensuring the reliability and credibility of the results. To tackle the persistent challenge of overfitting in deep learning models, we propose an innovative overfitting metric that considers the intricate relationship between training and testing errors, model complexity, and data complexity. This metric, in conjunction with the newly introduced generalization and robustness metrics, provides a holistic assessment of the model's performance, guiding the application of regularization techniques to maintain generalizability and resilience. Extensive experiments conducted on benchmark SER datasets demonstrate that the CENN model not only surpasses existing approaches in terms of accuracy but also sets a new benchmark in reproducibility. This work establishes a new paradigm for deep learning model development in SER, underscoring the vital importance of reproducibility and offering a rigorous framework for future research.

Study on seismic performance of the CFST-framed aeolian sand concrete composite shear wall

This study examined the seismic performance of composite shear walls framed with concrete-filled steel tubes (CFST) utilizing aeolian sand concrete. Six specimens were designed and tested under low cyclic load with a constant axial compression ratio. The research aimed to analyze how the CFST frame affects the seismic behavior of shear walls made from aeolian sand concrete and understand the underlying influence mechanism. The results showed significant differences in failure modes between CFST-framed and conventional aeolian sand concrete shear walls, with the former exhibiting a lower degree of failure and instances of plastic hinge failure. The hysteretic curve of CFST-framed walls displayed noticeable pinching, indicating enhanced seismic energy dissipation capacity and ductility. The damage analysis model based on the energy damage principle was found to be suitable for conducting seismic damage analysis of these walls. A shear capacity model was also established to accurately depict the variation in bearing capacity under low cyclic loading, providing valuable insights for engineering applications.

A novel forward performance-driven design method for gear parameters

Gear is one of the most crucial components of the transmission system, and the performance of gear directly affects the efficiency and reliability of the transmission system. Conventional methods for designing gear parameters involve several time-consuming and complex steps, which may not guarantee optimal performance. Therefore, we propose a new method for designing gear parameters that aims to improve efficiency and accuracy. First, the tooth surface equations of spur and helical involute gears suitable for symmetric and asymmetric teeth are deduced based on the gear-forming machining principle. Second, the performance evaluation models for load capacity, dynamic performance, efficiency, and power density of the gears are established based on the precise gear surface. The design objectives are standardized and evaluated comprehensively using a linear weighting method. Finally, a forward performance-driven design method of gear parameters is established. The proposed method is applied to a helical gear pair design case, and the results show that 90.7% of the individuals in the Pareto optimal front are asymmetric gears, with 9.3% being symmetric gears. This suggests that asymmetric gears have more opportunities to be optimal than symmetric gears. The highest-ranked gear designed using the proposed method is superior to the gear designed using conventional methods.

Predicting shear strength of fiber-reinforced concrete beams reinforced with longitudinal FRP bars with machine learning techniques

ABSTRACT This study investigates the shear strength prediction of Fibre-Reinforced Concrete (FRC) beams reinforced with Fibre-Reinforced Polymer (FRP) bars and without stirrups through the application of Machine Learning (ML) techniques. The utilisation of FRP bars, particularly Glass Fibre-Reinforced Polymer (GFRP) and Basalt Fibre-Reinforced Polymer (BFRP) bars, has emerged as a promising alternative to traditional steel reinforcement due to their superior mechanical properties and corrosion resistance. Moreover, the incorporation of macro- and micro-discrete fibres into concrete compositions enhances both shear and flexural behaviour while reducing crack propagation induced by tensile stresses. The primary objective of this research is to develop accurate predictive models for the shear capacity of FRP-reinforced concrete beams, considering various influential parameters. Six different ML techniques, namely Multiple Linear Regression (MLR), Gaussian Process Regression (GPR), k-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs) and Random Forest Regression (RFR), are employed to analyse the complex interactions between input variables, such as fibre type, bar diameter, concrete compressive strength, beam dimensions and the resulting shear strength. By leveraging these computational approaches, we aim to overcome the limitations of conventional analytical methods and provide robust predictions for structural design and optimisation.

Enhanced runoff simulation by precise capture of snowmelt variation signals with satellite-based snow products in a high-elevation basin

Hydrological models stand as a pivotal instrument for runoff simulation, while encountering notable uncertainties in output due to intricate terrain conditions and limited ground-based observations, especially in high-elevation basins. Leveraging satellite-based images presents a promising avenue for deciphering the hydrological model’s state variables. In pursuit of refining runoff simulation, this study developed a two-step enhancement strategy, which involved (1) calibrating snow-related parameters in the hydrological model using remotely sensed snow cover area (SCA) and snow water equivalent (SWE) and (2) capturing snowmelt runoff through the hydrological model and image-based products. Coupled with the Variable Infiltration Capacity (VIC) model, we adopted this strategy as a case study in the Dadu River Basin, China. The results indicated (1) the daily Nash-Sutcliffe Efficiency (NSE) of runoff simulation reached 0.84 by the enhancement strategy, markedly surpassing the strategy reliant on soil parameters with a single calibrated reference (daily NSE of 0.66), and exhibited comparability to the strategy incorporating snow parameters but calibrated solely based on observed discharge (daily NSE of 0.83). (2) the enhancement strategy demonstrated hydrological consistency with snowmelt information derived from imagery. Specifically, multi-year average contributions of model’s and image-based snowmelt calculations were 31.8% and 33.4%, respectively. Additionally, simulated dates of snow accumulation and ablation appeared an average deviation of approximately one week compared to the imagery results. This study elucidates a potential methodological approach for offering valuable insights into hydrological processes within analogous high-elevation basins globally.

Improving evapotranspiration partitioning by integrating satellite vegetation parameters into a land surface model

Land evapotranspiration (ET) primarily involves vegetation transpiration, canopy interception loss, and soil evaporation. Previous studies have made significant progress in total ET estimation; however, substantial challenges remain in partitioning ET on a regional scale, largely due to the intricate water and energy balance that is disrupted by vegetation cover changes. The accuracy of land surface models in representing ET components may be constrained by their inadequate consideration of vegetation dynamics. In this study, we integrate satellite leaf area index (LAI) and fraction of vegetation coverage (FVC) into the Variable Infiltration Capacity model (VIC) to improve ET partitioning ability in the Loess Plateau of China, a region that has experienced substantial vegetation dynamics. The results showed that satellite dynamic vegetation parameters in modeling are effective in improving the estimation of ET components compared with the default/static vegetation parameters. Considering LAI dynamics in the model enhances the representation of the inter- and intra-annual variations in vegetation transpiration and canopy interception loss. Dynamic FVC reasonably allocates transpiration to soil evaporation, capturing evaporation in forest gaps effectively. This effect is particularly relevant in arid and semi-arid regions. Among the ET components, transpiration was the most sensitive to the two dynamic vegetation parameters, followed by canopy interception loss and soil evaporation. Through the VIC model with dynamic vegetation parameters, our study revealed that soil evaporation was twice that of transpiration in the Loess Plateau, which is consistent with its semi-arid region and relatively sparse vegetation coverage. Our study offers valuable insights regarding the use of vegetation coverage for partitioning ET and highlights the advantage of integrating satellite vegetation products into land surface models.

High-noise solar panel defect identification method based on the improved EfficientNet-V2

As a crucial element in photovoltaic power generation systems, the condition of solar panels significantly impacts the efficiency of power generation. The ability to accurately and promptly detect defects in solar panels is essential for enhancing system performance. This study introduces a novel model for identifying defects in photovoltaic modules, leveraging an enhanced version of EfficientNet-V2. This model aims to address challenges in identifying defects in infrared images of solar panels under conditions of high-noise and low-model efficiency. To address the challenges of high image noise and blur, this article initially presents a methodology that combines the Db4 wavelet transform with a blind deconvolution algorithm for comprehensive preprocessing of the original image. Furthermore, this study optimizes the model's feature representation capabilities by implementing key transformations within the EfficientNet-V2 network framework. Notably, we replaced the traditional SE block with the more efficient channel attention (ECA) mechanism module. Due to its lightweight structure and effective performance, ECA substantially improves the model's capacity to extract complex and abstract image features, while also accelerating the training process's convergence speed and enhancing overall computational efficiency. At the classifier level, this paper innovatively integrates the XGBoost ensemble learning algorithm into the model, substituting the conventional softmax classifier used in traditional convolutional neural network (CNN). With its superior generalization capabilities, robust nonlinear modeling skills, and efficient computational characteristics, XGBoost can more accurately detect minute defects in solar panels based on the deep features produced by EfficientNet-V2, thereby significantly improving the accuracy and robustness of defect detection. Simulation results demonstrate that the proposed model structure outperforms traditional CNN models in terms of accuracy and stability, underscoring the efficacy of the enhanced EfficientNet-V2 model in detecting solar panel defects under high-noise conditions.

Characterization of load-bearing and failure properties of fractured rock masses reinforced by negative pressure grouting

Grouting injection is a vital technique for addressing the challenges of high stress and significant deformation in the surrounding rock during deep mining operations, playing a crucial role in promoting green and low-carbon extraction methodologies. In this study, grouting reinforcement processes were examined by conducting grouting experiments on a fractured rock with varying negative pressures (0-100 kPa), followed by uniaxial compression testing of the grout-reinforced bodies. This investigation explored the diffusion patterns of grout under negative pressure and established a constitutive model of damage-bearing capacity for bodies reinforced by negative pressure grouting. It further studied the enhancement effect of negative pressure on the load-bearing capacity of the reinforced bodies and analyzed the instability mechanism of damage and failure in these bodies. The results indicated that the diffusion of grout under negative pressure is influenced by four types of forces, which alter the extent of grout diffusion within the fractured rock mass. Introducing a damage constitutive model that serially connects pore and framework elements characterizes the damage and failure behavior of grout-reinforced bodies under different negative pressures. As the negative pressure increases, changes in porosity, water-to-cement ratio, and admixture quantity occur in the grout-reinforced specimens, with the strength mean curve showing a trend of first increasing and then decreasing, reaching a threshold at a negative pressure of 60 kPa. With increasing negative pressure, the negative pressure damage variable decreases and then increases, and the stronger the interfacial microelement connections caused by the negative pressure, the greater the bearing capacity, ultimately manifesting in different failure modes.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Coupling Soil Erosion and Sediment Transport Processes With the Variable Infiltration Capacity Model (VIC‐SED) for Applications Suitable With Coarse Spatial and Temporal Resolutions

AbstractUnderstanding soil erosion and sediment transport from the hillslope scale to the regional scale is crucial for studies on water quality, soil‐water conservation, the lateral carbon cycle, environmental zoning and vulnerability. However, most existing erosion and sediment transport models are only applicable at the hillslope scale or for small watersheds with fine spatial resolutions (typically much less than 1 km). This study presents a process‐based soil erosion and sediment transport model for model applications designed for applications with coarse spatial (e.g., ≥10 km) and temporal (e.g., from hourly to daily) resolutions. This new model, referred to as VIC‐SED, effectively accounts for interactions between erosion and hydrological processes. This is achieved by tightly coupling the erosion processes with a hydrologically based Three‐layer Variable Infiltration Capacity (VIC‐3L) land surface model (LSM) and to a multi‐scale routing (MSR) model. VIC‐SED considers the impacts of (a) the spatio‐temporal variability of rainfall intensity on erosion processes and (b) soil moisture on the soil detachment process. VIC‐SED is evaluated in two watersheds. Results demonstrate that VIC‐SED is capable of reproducing water and suspended sediment discharges at coarse spatial resolutions and varying temporal scales varying from 15‐min to daily intervals. Our study indicates that the VIC‐SED model is a promising tool for studying and assessing the impacts of climate and land cover changes on suspended sediment yields over large regions using coarse spatial and temporal resolutions.

Evaluating hydrological responses of satellite precipitation products over an Indian tropical catchment through a distributed physical model

AbstractSignificant advancements in satellite‐based precipitation retrieval algorithms have led to the development of continuous, quasi‐global precipitation products, offering unique opportunities for hydrometeorological and climate research. In this context, six satellite‐based precipitation products (SPPs), including CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were thoroughly investigated for their application in hydrological simulations over the Wardha River basin in India. The observed gridded precipitation product developed by India Meteorological Department (IMD) has been used as reference data to evaluate the performance of SPPs. Hydrological variables such as runoff, soil moisture (SM) and evapotranspiration (ET) were simulated using the Variable Infiltration Capacity model. The performances of SPPs are critically assessed using various statistical metrics, including Pearson's correlation coefficient (R), Klinga‐Gupta Efficiency (KGE), Percent Bias (PBIAS), root mean square error (RMSE), and the RMSE to standard deviation ratio (RSR). The model‐simulated discharge was compared with streamflow observations at a single gauging site, while a spatially distributed comparison was conducted between simulated SM and ET and satellite‐based SM and ET. The IMD dataset consistently shows superior performance for discharge simulation at both daily and monthly scales, with KGE values of 0.85 and 0.91, respectively. Among SPPs, MSWEP and CHIRPS excel in simulating daily and monthly discharge, respectively. For ET simulation, CHIRPS outperforms IMD and other SPPs, achieving an overall KGE value of 0.37. In contrast, PERSIANN is the most effective for simulating SM compared to other precipitation products.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.