scholarly journals The Suitability of the Satellite Metrological Inputs Source on the Hydrological Model in a Small Urban Catchment

2016 ◽  
Author(s):  
Song Song ◽  
Youpeng Xu ◽  
Jiali Wang ◽  
Jinkang Du ◽  
Jianxin Zhang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Yangtze River Delta ◽  
Precipitation Data ◽  
Model Quality ◽  
Small Catchment ◽  
Explanatory Variables ◽  
Distributed Models ◽  
The Impact

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

scholarly journals The effects of spatial discretization on performances and parameters of urban hydrological model

2019 ◽  
Vol 80 (3) ◽  
pp. 517-528 ◽  
Author(s):  
Qing Chang ◽  
So Kazama ◽  
Yoshiya Touge ◽  
Shunsuke Aita
Keyword(s):  
Spatial Resolution ◽  
Scale Effects ◽  
Model Performance ◽  
Computation Time ◽  
Rainfall Runoff ◽  
Total Runoff ◽  
Sensing Technology ◽  
Impervious Area ◽  
Runoff Modeling ◽  
The Impact

Abstract Selecting a proper spatial resolution for urban rainfall runoff modeling was not a trivial issue because it could affect the model outputs. Recently, the development of remote sensing technology and increasingly available data source had enabled rainfall runoff process to be modeled at detailed and microscales. However, the models with less complexity might have equally good performance with less model establishment and computation time. This study attempted to explore the impact of model spatial resolution on model performance and parameters. Models with different discretization degree were built up on the basis of actual drainage networks, urban parcels and specific land use. The results showed that there was very little difference in the total runoff volumes while peak flows showed obvious scale effects which could be up to 30%. Generally, model calibration could compensate the scale effect. The calibrated models with different resolution showed similar performances. The consideration of effective impervious area (EIA) as a calibration parameter marginally increased performance of the calibration period but also slightly decreased performance in the validation period which indicated the importance of detailed EIA identification.

scholarly journals The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem

2017 ◽  
Vol 17 (2) ◽  
pp. 1511-1528 ◽  
Author(s):  
Paola Crippa ◽  
Ryan C. Sullivan ◽  
Abhinav Thota ◽  
Sara C. Pryor
Keyword(s):  
Spatial Resolution ◽  
Model Performance ◽  
Specific Humidity ◽  
Spatiotemporal Variability ◽  
Limited Area ◽  
Monthly Precipitation ◽  
Regional Models ◽  
Skill Scores ◽  
The Impact ◽  
Aerosol Properties

Abstract. Limited area (regional) models applied at high resolution over specific regions of interest are generally expected to more accurately capture the spatiotemporal variability of key meteorological and climate parameters. However, improved performance is not inevitable, and there remains a need to optimize use of numerical resources and to quantify the impact on simulation fidelity that derives from increased resolution. The application of regional models for climate forcing assessment is currently limited by the lack of studies quantifying the sensitivity to horizontal spatial resolution and the physical–dynamical–chemical schemes driving the simulations. Here we investigate model skill in simulating meteorological, chemical and aerosol properties as a function of spatial resolution, by applying the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem) over eastern North America at different resolutions. Using Brier skill scores and other statistical metrics it is shown that enhanced resolution (from 60 to 12 km) improves model performance for all of the meteorological parameters and gas-phase concentrations considered, in addition to both mean and extreme aerosol optical depth (AOD) in three wavelengths in the visible relative to satellite observations, principally via increase of potential skill. Some of the enhanced model performance for AOD appears to be attributable to improved simulation of meteorological conditions and the concentration of key aerosol precursor gases (e.g., SO2 and NH3). Among other reasons, a dry bias in the specific humidity in the boundary layer and a substantial underestimation of total monthly precipitation in the 60 km simulations are identified as causes for the better performance of WRF-Chem simulations at 12 km.

scholarly journals Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths

10.29007/5hv1 ◽  
2018 ◽  
Author(s):  
Chuanzhe Li ◽  
Jia Liu ◽  
Fuliang Yu ◽  
Jiyang Tian ◽  
Yang Wang ◽  
...  
Keyword(s):  
Model Calibration ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Data Series ◽  
Observation Data ◽  
Calibration Data ◽  
Series Length ◽  
Streamflow Data ◽  
The Impact

This paper evaluates the effects of calibration data series length on the performance of a hydrological model in data-limited catchments where data are non-continuous and fragmental. Non-continuous calibration periods were used for more independent streamflow data for SIMHYD model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2016 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Research Group ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship

Shepperd et al. find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al.’s data. We observe that (a) research group shares a strong association with other explanatory variables (i.e., the dataset and metric families that are used to build a model); (b) the strong association among these explanatory variables makes it difficult to discern the impact of the research group on model performance; and (c) after mitigating the impact of this strong association, we find that the research group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model are more likely due to the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat any potential bias in their results.

scholarly journals Analysis of the impact of climate change on groundwater related hydrological fluxes: a multi-model approach including different downscaling methods

2011 ◽  
Vol 15 (1) ◽  
pp. 21-38 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
M. Butts ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Groundwater Resources ◽  
Small Catchment ◽  
Groundwater Levels ◽  
Northern Switzerland ◽  
Impact Of Climate Change ◽  
Hydrological Fluxes ◽  
The Impact

Abstract. Climate change related modifications in the spatio-temporal distribution of precipitation and evapotranspiration will have an impact on groundwater resources. This study presents a modelling approach exploiting the advantages of integrated hydrological modelling and a broad climate model basis. We applied the integrated MIKE SHE model on a perialpine, small catchment in northern Switzerland near Zurich. To examine the impact of climate change we forced the hydrological model with data from eight GCM-RCM combinations showing systematic biases which are corrected by three different statistical downscaling methods, not only for precipitation but also for the variables that govern potential evapotranspiration. The downscaling methods are evaluated in a split sample test and the sensitivity of the downscaling procedure on the hydrological fluxes is analyzed. The RCMs resulted in very different projections of potential evapotranspiration and, especially, precipitation. All three downscaling methods reduced the differences between the predictions of the RCMs and all corrected predictions showed no future groundwater stress which can be related to an expected increase in precipitation during winter. It turned out that especially the timing of the precipitation and thus recharge is very important for the future development of the groundwater levels. However, the simulation experiments revealed the weaknesses of the downscaling methods which directly influence the predicted hydrological fluxes, and thus also the predicted groundwater levels. The downscaling process is identified as an important source of uncertainty in hydrological impact studies, which has to be accounted for. Therefore it is strongly recommended to test different downscaling methods by using verification data before applying them to climate model data.

scholarly journals Assessing the impact of PET estimation methods on hydrologic model performance

2020 ◽  
Author(s):  
Dilhani Ishanka Jayathilake ◽  
Tyler Smith
Keyword(s):  
Potential Evapotranspiration ◽  
Model Performance ◽  
Monte Carlo Sampling ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Rainfall Runoff ◽  
Data Set ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energy- or water-limited using the Budyko framework and by eco-region, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

scholarly journals New Balanced Active Learning Model and Optimization Algorithm

2018 ◽  
Author(s):  
Xiaoqian Wang ◽  
Yijun Huang ◽  
Ji Liu ◽  
Heng Huang
Keyword(s):  
Experimental Design ◽  
Active Learning ◽  
Optimization Algorithm ◽  
Early Stage ◽  
Model Performance ◽  
Training Model ◽  
Learning Model ◽  
Model Quality ◽  
Learning Methods ◽  
The Impact

It is common in machine learning applications that unlabeled data are abundant while acquiring labels is extremely difficult. In order to reduce the cost of training model while maintaining the model quality, active learning provides a feasible solution. Instead of acquiring labels for random samples, active learning methods carefully select the data to be labeled so as to alleviate the impact from the redundancy or noise in the selected data and improve the trained model performance. In early stage experimental design, previous active learning methods adopted data reconstruction framework, such that the selected data maintained high representative power. However, these models did not consider the data class structure, thus the selected samples could be predominated by the samples from major classes. Such mechanism fails to include samples from the minor classes thus tends to be less "representative". To solve this challenging problem, we propose a novel active learning model for the early stage of experimental design. We use exclusive sparsity norm to enforce the selected samples to be (roughly) evenly distributed among different groups. We provide a new efficient optimization algorithm and theoretically prove the optimal convergence rate O(1/{T^2}). With a simple substitution, we reduce the computational load of each iteration from O(n^3) to O(n^2), which makes our algorithm more scalable than previous frameworks.

Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: application for a broadleaf-dominated forest in central Europe

2015 ◽  
Vol 45 (1) ◽  
pp. 111-123 ◽  
Author(s):  
Christoph Stepper ◽  
Christoph Straub ◽  
Hans Pretzsch
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Model Performance ◽  
Point Clouds ◽  
Training Data ◽  
Explanatory Variables ◽  
Growing Stock ◽  
Dense Point ◽  
Global Matching ◽  
The Impact

Dense image-based point clouds have great potential to accurately assess forest attributes such as growing stock. The objective of this study was to combine height and spectral information obtained from UltraCamXp stereo images to model the growing stock in a highly structured broadleaf-dominated forest (77.5 km2) in southern Germany. We used semi-global matching (SGM) to generate a dense point cloud and subtracted elevation values obtained from airborne laser scanner (ALS) data to compute canopy height. Sixty-seven explanatory variables were derived from the point cloud and an orthoimage for use in the model. Two different approaches — the linear regression model (lm) and the random forests model (rf) — were tested. We investigated the impact that varying amounts of training data had on model performance. Plot data from a previously acquired set of 1875 inventory plots was systematically eliminated to form three progressively less dense subsets of 937, 461, and 226 inventory plots. Model evaluation at the plot level (size: 500 m2) yielded relative root mean squared errors (RMSEs) ranging from 31.27% to 35.61% for lm and from 30.92% to 36.02% for rf. At the stand level (mean stand size: 32 ha), RMSEs from 14.76% to 15.73% for lm and from 13.87% to 14.99% for rf were achieved. Therefore, similar results were obtained from both modeling approaches. The reduction in the number of inventory plots did not considerably affect the precision. Our findings underline the potential for aerial stereo imagery in combination with ALS-based terrain heights to support forest inventory and management.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2016 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Research Group ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship

Shepperd et al. find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al.’s data. We observe that (a) research group shares a strong association with other explanatory variables (i.e., the dataset and metric families that are used to build a model); (b) the strong association among these explanatory variables makes it difficult to discern the impact of the research group on model performance; and (c) after mitigating the impact of this strong association, we find that the research group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model are more likely due to the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat any potential bias in their results.

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