scholarly journals Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Cihangir Koycegiz ◽  
Meral Buyukyildiz
Keyword(s):  
Assessment Tool ◽  
Swat Model ◽  
Data Driven ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Hydrologic Models ◽  
Closed Basin ◽  
Vector Machines ◽  
Physically Based ◽  
Validation Stage

Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R2) = 0.787 and Nash–Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models.

scholarly journals Spatio-temporal analysis of sediment yield with a physically based model for a data-scarce headwater in Konya Closed Basin, Turkey

2021 ◽  
Author(s):  
Cihangir Koycegiz ◽  
Meral Buyukyildiz ◽  
Serife Yurdagul Kumcu
Keyword(s):  
Sediment Yield ◽  
Assessment Tool ◽  
Swat Model ◽  
Temporal Analysis ◽  
Mathematical Methods ◽  
Closed Basin ◽  
Physically Based ◽  
Spatio Temporal ◽  
Sediment Data ◽  
Semi Empirical

Abstract There are many empirical, semi-empirical and mathematical methods that have been developed to estimate sediment yield by researchers. In the last decades, the advancement in computer technologies has increased the use of mathematical models as they can solve the system more rapidly and accurately. The Soil and Water Assessment Tool (SWAT) is one of the physically based hydrological models that is preferred to compute sediment yield. In this study, spatial and temporal analysis of sediment yield in the Çarşamba Stream located at the Konya Closed Basin has been investigated using the SWAT model. Streamflow and sediment data collected during the 2003–2015 time period have been used in the analysis. Consequently, the SWAT presented satisfactory results compared with R2 = 0.68, Nash–Sutcliffe Efficiency (NSE) = 0.68 in calibration and R2 = 0.76, NSE = 0.66 in validation. According to the model results, spatial asymmetry in terms of sediment yield was determined in the sub-basins of the study area.

scholarly journals SWAT Model Adaptability to a Small Mountainous Forested Watershed in Central Romania

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 860
Author(s):  
Nicu Constantin Tudose ◽  
Mirabela Marin ◽  
Sorin Cheval ◽  
Cezar Ungurean ◽  
Serban Octavian Davidescu ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Hydrological Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Decision Makers ◽  
Forested Watershed ◽  
Sensitive Parameters ◽  
Lower Uncertainty ◽  
Validation Stage ◽  
Water Assessment

This study aims to build and test the adaptability and reliability of the Soil and Water Assessment Tool hydrological model in a small mountain forested watershed. This ungauged watershed covers 184 km2 and supplies 90% of blue water for the Brașov metropolitan area, the second largest metropolitan area of Romania. After building a custom database at the forest management compartment level, the SWAT model was run. Further, using the SWAT-CUP software under the SUFI2 algorithm, we identified the most sensitive parameters required in the calibration and validation stage. Moreover, the sensitivity analysis revealed that the surface runoff is mainly influenced by soil, groundwater and vegetation condition parameters. The calibration was carried out for 2001‒2010, while the 1996‒1999 period was used for model validation. Both procedures have indicated satisfactory performance and a lower uncertainty of model results in replicating river discharge compared with observed discharge. This research demonstrates that the SWAT model can be applied in small ungauged watersheds after an appropriate parameterisation of its databases. Furthermore, this tool is appropriate to support decision-makers in conceiving sustainable watershed management. It also guides prioritising the most suitable measures to increase the river basin resilience and ensure the water demand under climate change.

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

scholarly journals A New Physically-Based Spatially-Distributed Groundwater Flow Module for SWAT+

Hydrology ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 75
Author(s):  
Ryan T. Bailey ◽  
Katrin Bieger ◽  
Jeffrey G. Arnold ◽  
David D. Bosch
Keyword(s):  
Water Balance ◽  
Groundwater Flow ◽  
Land Surface ◽  
Assessment Tool ◽  
Swat Model ◽  
Watershed Model ◽  
Groundwater Head ◽  
Physically Based ◽  
Spatially Distributed ◽  
Modeling Code

Watershed models are used worldwide to assist with water and nutrient management under conditions of changing climate, land use, and population. Of these models, the Soil and Water Assessment Tool (SWAT) and SWAT+ are the most widely used, although their performance in groundwater-driven watersheds can sometimes be poor due to a simplistic representation of groundwater processes. The purpose of this paper is to introduce a new physically-based spatially-distributed groundwater flow module called gwflow for the SWAT+ watershed model. The module is embedded in the SWAT+ modeling code and is intended to replace the current SWAT+ aquifer module. The model accounts for recharge from SWAT+ Hydrologic Response Units (HRUs), lateral flow within the aquifer, Evapotranspiration (ET) from shallow groundwater, groundwater pumping, groundwater–surface water interactions through the streambed, and saturation excess flow. Groundwater head and groundwater storage are solved throughout the watershed domain using a water balance equation for each grid cell. The modified SWAT+ modeling code is applied to the Little River Experimental Watershed (LREW) (327 km2) in southern Georgia, USA for demonstration purposes. Using the gwflow module for the LREW increased run-time by 20% compared to the original SWAT+ modeling code. Results from an uncalibrated model are compared against streamflow discharge and groundwater head time series. Although further calibration is required if the LREW model is to be used for scenario analysis, results highlight the capabilities of the new SWAT+ code to simulate both land surface and subsurface hydrological processes and represent the watershed-wide water balance. Using the modified SWAT+ model can provide physically realistic groundwater flow gradients, fluxes, and interactions with streams for modeling studies that assess water supply and conservation practices. This paper also serves as a tutorial on modeling groundwater flow for general watershed modelers.

scholarly journals Use of the swat model for hydro-sedimentologic simulation in a small rural watershed

2012 ◽  
Vol 36 (2) ◽  
pp. 557-565 ◽  
Author(s):  
Talita Uzeika ◽  
Gustavo H Merten ◽  
Jean P.G Minella ◽  
Michele Moro
Keyword(s):  
Land Use Planning ◽  
Cover Crops ◽  
Assessment Tool ◽  
Swat Model ◽  
Conservation Agriculture ◽  
The United States ◽  
Daily Basis ◽  
Efficiency Coefficient ◽  
Monthly Basis ◽  
Sediment Yields

Mathematical models have great potential to support land use planning, with the goal of improving water and land quality. Before using a model, however, the model must demonstrate that it can correctly simulate the hydrological and erosive processes of a given site. The SWAT model (Soil and Water Assessment Tool) was developed in the United States to evaluate the effects of conservation agriculture on hydrological processes and water quality at the watershed scale. This model was initially proposed for use without calibration, which would eliminate the need for measured hydro-sedimentologic data. In this study, the SWAT model was evaluated in a small rural watershed (1.19 km²) located on the basalt slopes of the state of Rio Grande do Sul in southern Brazil, where farmers have been using cover crops associated with minimum tillage to control soil erosion. Values simulated by the model were compared with measured hydro-sedimentological data. Results for surface and total runoff on a daily basis were considered unsatisfactory (Nash-Sutcliffe efficiency coefficient - NSE < 0.5). However simulation results on monthly and annual scales were significantly better. With regard to the erosion process, the simulated sediment yields for all years of the study were unsatisfactory in comparison with the observed values on a daily and monthly basis (NSE values < -6), and overestimated the annual sediment yield by more than 100 %.

scholarly journals A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yunlin Guan ◽  
Yun Wang ◽  
Xuedong Yan ◽  
Haonan Guo ◽  
Yu Zhou
Keyword(s):  
Big Data ◽  
Clustering Algorithms ◽  
Demand Estimation ◽  
Accurate Estimate ◽  
Data Driven ◽  
Support Vector ◽  
Regression Problem ◽  
Vector Machines ◽  
Zone Division ◽  
Parking Demand

Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.

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