Comparison and regionalization of hydrologically based instream flow techniques in Atlantic Canada

1995 ◽  
Vol 22 (2) ◽  
pp. 235-246 ◽  
Author(s):  
Daniel Caissie ◽  
Nassir El-Jabi
Keyword(s):  
Regression Analysis ◽  
Base Flow ◽  
Low Flow ◽  
Coefficient Of Determination ◽  
Atlantic Canada ◽  
Ungauged Basins ◽  
Instream Flow ◽  
Regional Study ◽  
Explanatory Variables ◽  
Tennant Method

Five hydrologically based instream flow assessment methods are compared for 70 rivers in Atlantic Canada; these methods included (i) Tennant method; (ii) 25% mean annual flow (25% MAF); (iii) median monthly flow (Q50) which includes the aquatic base flow (ABF); (iv) the flow equalled or exceeded 90% of time on a monthly flow duration curve (Q90); and (v) the statistical 7-day low flow frequency of a 10-year recurrence interval (7Q10). By comparing the different methods relative to the 25% MAF (the commonly used method in Atlantic Canada), we found that the Q90 and 7Q10 methods predicted extremely low instream flows during winter and summer months. Resource management decisions based on these extremely low flow predictions could have serious adverse consequences. The median monthly flow method (Q50) was recommended for gauged basins, whereas the Tennant method, the 25% MAF method, and the ABF methods were recommended for ungauged basins. For ungauged basins, we conducted a regional study to estimate the 25% MAF and the ABF using multiple regression analysis. Physiographic parameters were used as explanatory variables in the regression analysis. Based on the coefficient of determination, R2, the best regression results were obtained for the 25% MAF with R2 ranging from 0.957 to 0.999. Although the results for ABF were slightly lower than for the 25% MAF, R2 was still in the range of 0.868 to 0.979. Key words: environmental assessment, maintenance flow, low flow, aquatic resources.

scholarly journals Multiple causes of nonstationarity in the Weihe annual low-flow series

2018 ◽  
Vol 22 (2) ◽  
pp. 1525-1542 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Potential Evapotranspiration ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Distribution Model ◽  
Flow Index ◽  
Explanatory Variables ◽  
Distribution Parameters

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced various nonstationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, through a nonstationary frequency analysis framework with the generalized linear model (GLM) to consider time-varying distribution parameters, the multiple explanatory variables were incorporated to explain the variation in low-flow distribution parameters. These variables are comprised of the three indices of human activities (HAs; i.e., population, POP; irrigation area, IAR; and gross domestic product, GDP) and the eight measuring indices of the climate and catchment conditions (i.e., total precipitation P, mean frequency of precipitation events λ, temperature T, potential evapotranspiration (EP), climate aridity index AIEP, base-flow index (BFI), recession constant K and the recession-related aridity index AIK). This framework was applied to model the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China (also known as the Wei He River). The results from stepwise regression for the optimal explanatory variables show that the variables related to irrigation, recession, temperature and precipitation play an important role in modeling. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that the nonstationary distribution model with any one of all explanatory variables is better than the one without explanatory variables, the nonstationary gamma distribution model with four optimal variables is the best model and AIK is of the highest relative importance among these four variables, followed by IAR, BFI and AIEP. We conclude that the incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to analyze future occurrences of low-flow extremes in similar areas.

scholarly journals Continuous Modeling of the Mkurumudzi River Catchment in Kenya Using the HEC-HMS Conceptual Model: Calibration, Validation, Model Performance Evaluation and Sensitivity Analysis

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 44 ◽  
Author(s):  
Wendso Ouédraogo ◽  
James Raude ◽  
John Gathenya
Keyword(s):  
Sensitivity Analysis ◽  
Stream Flow ◽  
Model Performance ◽  
Base Flow ◽  
Low Flow ◽  
Coefficient Of Determination ◽  
Flow Index ◽  
Model Parameters ◽  
Time Step ◽  
Continuous Modeling

The Mkurumudzi River originates in the Shimba hills and runs through Kwale County on the Kenyan Coast. Study on this river has been informed by the many economic activities that the river supports, which include sugarcane plantations, mining, tourism and subsistence farming. The main objective of this study was to use the soil moisture accounting (SMA) model specified in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) settings for the continuous modeling of stream flow in the Mkurumudzi catchment. Data from past years were compared with observed stream flow data in order to evaluate whether the model can be used for further prediction. The calibration was performed using data from 1988 to 1991 and validation for the period from 1992 to 1995 at a daily time step. The model performance was evaluated based on computed statistical parameters and visual checking of plotted hydrographs. For the calibration period of the continuous modeling, the performance of the model was very good, with a coefficient of determination R2 = 0.80, Nash-Sutcliffe Efficiency NSE = 0.80, index of agreement d = 0.94, and a Root Mean Squared Error (RMSE)/observations’ standard deviation ratio—RSR = 0.46. Similarly, the continuous model performance for the validation period was good, with R2 = 0.67, NSE = 0.65, RSR = 0.62 and d = 0.88. Based on these performance results, the SMA model in the HEC-HMS was found to give a satisfactory prediction of stream flow in the Mkurumudzi Catchment. The sensitivity analysis of the model parameters was performed, and the different parameters were ranked according to their sensitivity in terms of percent change in simulated runoff volume, peaks, Nash-Efficiency, seven-day low flow and base flow index. Sensitivity analysis helped to understand the relationships between the key model parameters and the variables.

scholarly journals Multiple Causes of Nonstationarity in the Weihe Annual Low Flow Series

2017 ◽  
Author(s):  
Bin Xiong ◽  
Lihua Xiong ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Lingqi Li
Keyword(s):  
Frequency Analysis ◽  
Driving Forces ◽  
Aridity Index ◽  
Base Flow ◽  
Low Flow ◽  
Flow Index ◽  
Time Varying ◽  
Explanatory Variables ◽  
Flow Generation ◽  
Flow Frequency

Abstract. Under the background of global climate change and local anthropogenic activities, multiple driving forces have introduced a variety of non-stationary components into low-flow series. This has led to a high demand on low-flow frequency analysis that considers nonstationary conditions for modeling. In this study, a nonstationary framework of low-flow frequency analysis has been developed on basis of the Generalized Linear Model (GLM) to consider time-varying distribution parameters. In GLMs, the candidate explanatory variables to explain the time-varying parameters are comprised of the eight measuring indices of the climate and catchment conditions in low flow generation, i.e., total precipitation (P), mean frequency of precipitation events (λ), temperature (T), potential evapotranspiration (ET), climate aridity index (AIET), base-flow index (BFI), recession constant (K) and the recession-related aridity index (AIK). This framework was applied to the annual minimum flow series of both Huaxian and Xianyang gauging stations in the Weihe River, China. Stepwise regression analysis was performed to obtain the best subset of those candidate explanatory variables for the final optimum model. The results show that the inter-annual variability in the variables of those selected best subsets plays an important role in modeling annual low flow series. Specifically, analysis of annual minimum 30-day flow in Huaxian shows that AIK is of the highest relative importance among the best subset of eight candidates, followed by BFI and AIET. The incorporation of multiple indices related to low-flow generation permits tracing various driving forces. The established link in nonstationary analysis will be beneficial to predict future occurrences of low-flow extremes in similar areas.

FRAUD CONFIRMATION, CLIENT SATISFACTION AND CLIENT LOYALTY

2019 ◽  
Vol 1 (1) ◽  
pp. 39
Author(s):  
Ngurah Pandji Mertha Agung Durya
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Client Satisfaction ◽  
Audit Quality ◽  
Hypothesis Test ◽  
Linear Regression Analysis ◽  
Quality Attributes ◽  
Path Model ◽  
Coefficient Of Determination ◽  
The Government

<p>This study aims to find evidence, the influence of Audit Quality Attributes, Client Satisfaction and Client Loyalty, which are moderated by Fraud Confirmation. The research was conducted at the BKM, a community-based organization, formed by the Government, through the <em>Kotaku</em> Program. The research used Regression statistical analysis and conducted a hypothesis test. Regression analysis used includes Simple Linear Regression Analysis, Multiple Regression Analysis, and MRA Regression Analysis, and Path Model Linear Regression Analysis. This study also pays attention to the calculation of the coefficient of determination to give an idea of the ability of the model in explaining the phenomenon of Client Satisfaction and Client Loyalty. The result that both partially and simultaneously, Audit Quality Attributes, Fraud Confirmation affected Client Satisfaction and Loyalty. The research also succeeded in proving that Client Satisfaction mediates the effect of Audit Quality Attributes on Client Loyalty, but failed to provide empirical evidence, that the Fraud Confirmation moderated the effect of Audit Quality Attributes on Client Satisfaction and Loyalty. Contribution to audit practices, where it is important to realize Client Satisfaction through Audit Quality Attributes and Fraud Confirmation, especially in situations where Fraud acts are suspected.</p>

Faktor – Faktor Yang Mempengaruhi Minat Berwirausaha Mahasiswa Program Studi Manajemen Fakultas Ekonomi Universitas Flores

Analisis ◽  
2020 ◽  
Vol 19 (1) ◽  
pp. 76-84
Author(s):  
Nasarius Aban ◽  
Gabriel Tanusi
Keyword(s):  
Emotional Intelligence ◽  
Regression Analysis ◽  
Family Environment ◽  
Linear Regression Analysis ◽  
Simple Random Sampling ◽  
Coefficient Of Determination ◽  
Test Results ◽  
Study Program ◽  
Management Study ◽  
The University

This study aims to determine the effect of emotional intelligence, independent attitude and family environment on the interest in entrepreneurship at the University of Flores Management Faculty of Economics. This research is an associative research. The population in this study were students of the Management Study Program of the Faculty of Economics of the University of Flores in the class of 2015-2016 who had passed the entrepreneurship courses of 170 people. Samples taken in this study were 105 respondents, with sampling techniques using simple random sampling. Data collection using questionnaires and interviews, while data analysis was performed using multiple linear regression analysis. The results of multiple regression analysis are Y = 1.060 + 0.594X1 + 0.114X2 + 0.421X3 + e. The coefficient of determination R2 for the variables X1, X2, X3 is 0.675, which means that entrepreneurial interest can be influenced by emotional intelligence, independent attitude and family environment by 67.50% and the remaining 32.50% is influenced by other factors including factors of education, skills, motivation and others. F test results show the value of Fcount> Ftable (28.442> 2.69) with a significant level of 0.000 <0.05 meaning that there is a positive and significant influence between emotional intelligence, independent attitude and family environment together on the entrepreneurial interest of the Faculty of Management Study Program Students The economy. Partial test results (t) show 1) Emotional intelligence factors have a positive and significant effect on entrepreneurial interest 2) Family environment factors have a positive and significant effect on entrepreneurial interest 3) Independent attitude factor has no positive and significant effect on entrepreneurial interest.

scholarly journals Estimation of recharge in mountain hard-rock aquifers based on discrete spring discharge monitoring during base-flow recession

2021 ◽  
Author(s):  
Stefano Segadelli ◽  
Maria Filippini ◽  
Anna Monti ◽  
Fulvio Celico ◽  
Alessandro Gargini
Keyword(s):  
Hard Rock ◽  
Monitoring Program ◽  
Base Flow ◽  
Low Flow ◽  
Reliable Estimate ◽  
Spring Discharge ◽  
Aquifer Recharge ◽  
Clear Cut ◽  
Groundwater Circulation ◽  
Hard Rock Aquifers

AbstractEstimation of aquifer recharge is key to effective groundwater management and protection. In mountain hard-rock aquifers, the average annual discharge of a spring generally reflects the vertical aquifer recharge over the spring catchment. However, the determination of average annual spring discharge requires expensive and challenging field monitoring. A power-law correlation was previously reported in the literature that would allow quantification of the average annual spring discharge starting from only a few discharge measurements in the low-flow season, in a dry summer climate. The correlation is based upon the Maillet model and was previously derived by a 10-year monitoring program of discharge from springs and streams in hard-rock aquifers composed of siliciclastic and calcareous turbidites that did not have well defined hydrogeologic boundaries. In this research, the same correlation was applied to two ophiolitic (peridotitic) hard-rock aquifers in the Northern Apennines (Northern Italy) with well-defined hydrogeologic boundaries and base-outflow springs. The correlation provided a reliable estimate of the average annual spring discharge thus confirming its effectiveness regardless of bedrock lithology. In the two aquifers studied, the measurable annual outputs (i.e. sum of average annual spring discharges) could be assumed equal to the annual inputs (i.e. vertical recharge) based on the clear-cut aquifer boundaries and a quick groundwater circulation inferable from spring water parameters. Thus, in such setting, the aforementioned correlation also provided an estimate of the annual aquifer recharge allowing the assessment of coefficients of infiltration (i.e. ratio between aquifer recharge and total precipitation) ranging between 10 and 20%.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tomoyuki Takura ◽  
Keiko Hirano Goto ◽  
Asao Honda
Keyword(s):  
Regression Analysis ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Long Term Care ◽  
Healthcare Resource ◽  
Resource Consumption ◽  
Coefficient Of Determination ◽  
Term Care ◽  
Adherence Score

Abstract Background Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. Methods This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. Results Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). Conclusions ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

GAYA KEPEMIMPINAN DAN TINGKAT STRES KERJA KARYAWAN PERUSAHAAN TELEKOMUNIKASI INDONESIA

2018 ◽  
Vol 12 (2) ◽  
pp. 137
Author(s):  
Elvira Azis ◽  
Arif Partono Prasetio ◽  
Lugina Suciati Putri ◽  
Annisa Yasya Zhafira
Keyword(s):  
Regression Analysis ◽  
Leadership Style ◽  
Work Stress ◽  
Stress Level ◽  
Coefficient Of Determination ◽  
Management Service ◽  
Transportation Management ◽  
Dependent Variables ◽  
Simple Regression Analysis ◽  
Simple Regression

ABSTRACTThe study investigates the effect of leadership style on employee’s work stress in ESS Transportation Management Service Telkom. The data were collected from 92 employees and obtained using a questionnaire consisted of 29 items with 6 point Likert scale. Desciptive analysis used to illustratedthe leadership style and work stress level inside the organization. Simple regression analysis was used to analyze the data and to measure the relation between independent and dependent variables. Employee perceives that the leadership style implemented in the organization was already appropriate and in line with their expectation regarding how their leader should engage the work relation. Meanwihle, the work stress level among employees were low. The regression analysis revealed the negative relation between leadership style and work stress level. When employeesperceived that their leader was act accordance to what their expectaion then the stress level will lessen. The coefficient of determination was 0.321. This mean the leadership style can only explain 32.1% of work stress, then the organization need to identify other factors which also affect the work stress. The comparison with the previous literatures also discussed.ABSTRAKPenelitian ini bertujuan menganalisis pengaruh gaya kepemimpinan terhadap tingkat stres kerja karyawan di Divisi ESS Transportation Management Service Telkom. Data penelitian diperoleh daripenyebaran kuesioner terhadap 92 orang responden. Kuesioner yang digunakan memiliki 29 pertanyaan dengan skala Likert 6 poin. Teknik analisis deskriptif digunakan untuk menjelaskan tingkat gaya kepemimpinan dan tingkat stres yang dipersepsikan oleh karyawan. Sedangkan untuk menganalisis pengaruh antara dua ariabel tersebut digunakan analisis regresi sederhana. Hasil penelitian mengungkapkan bahwa karyawan merasa gaya kepemimpinan yang diterapkan sudahsesuai dengan apa yang mereka harapkan. Mereka menilai bahwa pemimpin sudah menerapkan gaya yang tepat dalam setiap kondisi yang terkait pekerjaan. Tingkat stres karyawan di perusahaan ini relatif rendah. Selanjutnya, analisis regresi memperlihatkan hubungan signifikan negatif antara gaya kepemimpinan dan tingkat stres. Karyawan yang merasa sesuai dengan gaya kepemimpinan atasan akan memiliki tingkat stres yang lebih rendah. Koefisien determinasi dari hasil penelitian ini adalah 0.321. ini berarti bahwa gaya kepemimpinan hanya dapat menjelaskan tingkat stres sebesar 32.1% of work stress. Hasil ini menjadi masukan bagi perusahaan untuk mempelajari faktor-faktor lain penyebab stres. Pembahasan di dalam penelitian ini akan menyajikan pula hasil riset terdahulu dari berbagai latar belakang budaya

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