scholarly journals Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 823 ◽  
Author(s):  
JungJin Kim ◽  
Jae Ryu
Keyword(s):  
Parallel Computing ◽  
Execution Time ◽  
Hydrological Modeling ◽  
Cost Effective ◽  
Hydrologic Model ◽  
Coefficient Of Determination ◽  
Partial Sums ◽  
Urban Rural ◽  
Set Up

The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study.

scholarly journals Hydrological Modeling of the Paligad Watershed (India) Using HSFP Model

2019 ◽  
pp. 217-228
Author(s):  
Aamir Ishaq Shah ◽  
Sumit Sen ◽  
Anurag Mishra
Keyword(s):  
Hydrological Modeling ◽  
Simulation Program ◽  
Expert Advice ◽  
Coefficient Of Determination ◽  
Hydrological Simulation ◽  
Hspf Model ◽  
Hydrological System ◽  
Set Up

For hydrological studies, it is well known that each hydrological system behaves differently and in order to effectively manage those systems, it is necessary to understand their behavior. The hydrological component of Hydrological Simulation Program – FORTRAN (HSPF) model was set up and calibrated for Paligad watershed which is a sub-basin of Aglar watershed in the Uttarakhand state of India. The calibration of the model was done manually and an expert advice system called as HSPEXP+ was used to aid calibration. The values of evaluation indicators such as coefficient of determination (

Leveraging Social Media in Facilitating Women Entrepreneurs in India

2020 ◽  
Vol 10 (2) ◽  
pp. 76-90
Author(s):  
Madhura Manish Bedarkar ◽  
Mahima Mishra ◽  
Ritesh Ashok Khatwani
Keyword(s):  
Social Media ◽  
Women Entrepreneurs ◽  
Cost Effective ◽  
Psychological Benefits ◽  
Study Approach ◽  
Self Confidence ◽  
Social Media Platforms ◽  
Set Up

This article explores the role of social media in facilitating women entrepreneurs in India. It adopts a case study approach to explore the effectiveness of social media platforms in supporting women entrepreneurs. PULA (Pune Ladies), a closed Facebook Group, set up in 2015 for women in Pune, was selected as a case study. Fifteen in-depth interviews were conducted among 15 active women entrepreneurs of this group to explore the benefits received in terms of visibility, marketing opportunities, revenue generation, psychological benefits (sense of belongingness, self-confidence, motivation), and counselling to name a few. Their responses were analyzed for commonalities and divergences. The article finds that PULA not only offers a cost-effective platform for women entrepreneurs to showcase their products/services but also helps them in enhancing the visibility and financial performance of their businesses. The findings of this study will guide women entrepreneurs in leveraging social media platforms through greater visibility, networking and marketing their products/ services more efficiently.

scholarly journals Simultaneously determining global sensitivities of model parameters and model structure

2020 ◽  
Vol 24 (12) ◽  
pp. 5835-5858
Author(s):  
Juliane Mai ◽  
James R. Craig ◽  
Bryan A. Tolson
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Structural Model ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Framework ◽  
Model Structure Uncertainty ◽  
Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply the extended Sobol' sensitivity analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that (1) the xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability, while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration and uncertainty analysis. (4) The analysis of time-dependent process sensitivities regarding simulated streamflow is a helpful tool for understanding model internal dynamics over the course of the year.

scholarly journals A cost-effective IoT strategy for remote deployment of soft sensors – a case study on implementing a soft sensor in a multistage MBBR plant

2020 ◽  
Vol 81 (8) ◽  
pp. 1733-1739 ◽  
Author(s):  
A. M. Nair ◽  
A. Hykkerud ◽  
H. Ratnaweera
Keyword(s):  
Open Source ◽  
Cost Effective ◽  
Data Access ◽  
Quality Parameters ◽  
Soft Sensor ◽  
Biofilm Reactor ◽  
Remote Access ◽  
Soft Sensors ◽  
Set Up

Abstract Model-based soft sensors can enhance online monitoring in wastewater treatment processes. These soft sensor scripts are executed either locally on a programmable logic controller (PLC) or remotely on a system with data-access over the internet. This work presents a cost-effective, flexible, open source IoT solution for remote deployment of a soft sensing algorithm. The system uses low-priced hardware and open-source programming language to set up the communication and remote-access system. Advantages of the new IoT architecture are demonstrated through a case study for remote deployment of an Extended Kalman Filter (EKF) to estimate additional water quality parameters in a multistage moving bed biofilm reactor (MBBR) plant. The soft-sensor results are successfully validated against standardised laboratory measurements to prove their ability to provide real-time estimations.

An Application of Multivariate Statistical Techniques for Cost Effective Decision Making ­ A Case Study

2005 ◽  
Vol 57 (1-2) ◽  
pp. 109-120
Author(s):  
Biswabrata Pradhan
Keyword(s):  
Cost Benefit Analysis ◽  
Function Analysis ◽  
Cost Benefit ◽  
Cost Effective ◽  
Classification Rule ◽  
Multivariate Statistical ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Set Up

This paper focuses on the application of multivariate statical techniques for making a cost effective decision in an industrial set up. The objective of this study is to take a decision with respect to several parameters whether a particular product can be sent to the customer or not. The techniques like MANOVA, discriminant and classification function analysis have been used to fulfil the objectives. An optimum classification rule has been established for making the decision. A cost benefit analysis has also been done after iniplementing the proposed optimum decision­making rule.

scholarly journals Simultaneously Determining Global Sensitivities of Model Parameters and Model Structure

2020 ◽  
Author(s):  
Juliane Mai ◽  
James R. Craig ◽  
Bryan A. Tolson
Keyword(s):  
Sensitivity Analysis ◽  
Hydrological Modeling ◽  
Structural Model ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Framework ◽  
Model Structure Uncertainty ◽  
Model Variability

Abstract. Model structure uncertainty is known to be one of the three main sources of hydrologic model uncertainty along with input and parameter uncertainty. Some recent hydrological modeling frameworks address model structure uncertainty by supporting multiple options for representing hydrological processes. It is, however, still unclear how best to analyze structural sensitivity using these frameworks. In this work, we apply an Extended Sobol' Sensitivity Analysis (xSSA) method that operates on grouped parameters rather than individual parameters. The method can estimate not only traditional model parameter sensitivities but is also able to provide measures of the sensitivities of process options (e.g., linear vs. non-linear storage) and sensitivities of model processes (e.g., infiltration vs. baseflow) with respect to a model output. Key to the xSSA method's applicability to process option and process sensitivity is the novel introduction of process option weights in the Raven hydrological modeling framework. The method is applied to both artificial benchmark models and a watershed model built with the Raven framework. The results show that: (1) The xSSA method provides sensitivity estimates consistent with those derived analytically for individual as well as grouped parameters linked to model structure. (2) The xSSA method with process weighting is computationally less expensive than the alternative aggregate sensitivity analysis approach performed for the exhaustive set of structural model configurations, with savings of 81.9 % for the benchmark model and 98.6 % for the watershed case study. (3) The xSSA method applied to the hydrologic case study analyzing simulated streamflow showed that model parameters adjusting forcing functions were responsible for 42.1 % of the overall model variability while surface processes cause 38.5 % of the overall model variability in a mountainous catchment; such information may readily inform model calibration. (4) The analysis of time dependent process sensitivities regarding simulated streamflow is a helpful tool to understand model internal dynamics over the course of the year.

scholarly journals Evaluation and Hydrological Application of a Data Fusing Method of Multi-Source Precipitation Products-A Case Study Over Tuojiang River Basin

2021 ◽  
Vol 13 (13) ◽  
pp. 2630
Author(s):  
Yao Li ◽  
Wensheng Wang ◽  
Guoqing Wang ◽  
Siyi Yu
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Weighted Average ◽  
Probability Of Detection ◽  
Coefficient Of Determination ◽  
Maximum Probability ◽  
Satellite Precipitation

Precipitation is an essential driving factor of hydrological models. Its temporal and spatial resolution and reliability directly affect the accuracy of hydrological modeling. Acquiring accurate areal precipitation needs substantial ground rainfall stations in space. In many basins, ground rainfall stations are sparse and uneven, so real-time satellite precipitation products (SPPs) have become an important supplement to ground-gauged precipitation (GGP). A multi-source precipitation fusion method suitable for the Soil and Water Assessment Tool (SWAT) model has been proposed in this paper. First, the multivariate inverse distance similarity method (MIDSM) was proposed to search for the optimal representative precipitation points of GGP and SPPs in sub-basins. Subsequently, the correlation-coefficient-based weighted average method (CCBWA) was presented and applied to calculate the fused multi-source precipitation product (FMSPP), which combined GGP and multiple satellite precipitation products. The effectiveness of the FMSPP was proven over the Tuojiang River Basin. In the case study, three SPPs were chosen as the satellite precipitation sources, namely the Climate Forecast System Reanalysis (CFSR), Tropical Rainfall Measuring Mission Project (TRMM), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR). The evaluation indicators illustrated that FMSPP could capture the occurrence of rainfall events very well, with a maximum Probability of Detection (POD) and Critical Success Index (CSI) of 0.92 and 0.83, respectively. Furthermore, its correlation with GGP, changing in the range of 0.84–0.96, was higher in most sub-basins on the monthly scale than the other three SPPs. These results demonstrated that the performance of FMSPP was the best compared with the original SPPs. Finally, FMSPP was applied in the SWAT model and was found to effectively drive the SWAT model in contrast with a single precipitation source. The FMSPP manifested the highest accuracy in hydrological modeling, with the Coefficient of Determination (R2) of 0.84, Nash Sutcliff (NS) of 0.83, and Percent Bias (PBIAS) of only −1.9%.

Knee Muscle Force Estimating Model Using Machine Learning Approach

2020 ◽  
Author(s):  
Anurag Sohane ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Muscle Force ◽  
Vastus Lateralis ◽  
Input Parameter ◽  
Research Work ◽  
Cost Effective ◽  
Coefficient Of Determination ◽  
Muscle Forces ◽  
Knee Muscle

Abstract Various simulation type tools and conventional algorithms are being used to determine knee muscle forces of human during dynamic movement. These all may be good for clinical uses, but have some drawbacks, such as higher computational times, muscle redundancy and less cost-effective solution. Recently, there has been an interest to develop supervised learning-based prediction model for the computationally demanding process. The present research work is used to develop a cost-effective and efficient machine learning (ML) based models to predict knee muscle force for clinical interventions for the given input parameter like height, mass and angle. A dataset of 500 human musculoskeletal, have been trained and tested using four different ML models to predict knee muscle force. This dataset has obtained from anybody modeling software using AnyPyTools, where human musculoskeletal has been utilized to perform squatting movement during inverse dynamic analysis. The result based on the datasets predicts that the random forest ML model outperforms than the other selected models: neural network, generalized linear model, decision tree in terms of mean square error (MSE), coefficient of determination (R2), and Correlation (r). The MSE of predicted vs actual muscle forces obtained from the random forest model for Biceps Femoris, Rectus Femoris, Vastus Medialis, Vastus Lateralis are 19.92, 9.06, 5.97, 5.46, Correlation are 0.94, 0.92, 0.92, 0.94 and R2 are 0.88, 0.84, 0.84 and 0.89 for the test dataset, respectively.

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