Analysis of the Impacts of Spatial Input Data Quality on the Determination of Runoff and Suspended Sediments in the Imha Watershed Using Swat Model

Size: px
Start display at page:

Download "Analysis of the Impacts of Spatial Input Data Quality on the Determination of Runoff and Suspended Sediments in the Imha Watershed Using Swat Model"

Transcription

1 Analyss of the Impacts of Spatal Input Data ualty on the Determnaton of Runoff and Suspended Sedments n the Imha Watershed Usng Swat Model Jeongkon Km, Joonwoo Noh, Kyungho Son, Ikjae Km K-water Insttute

2 Background Landslde Bank Washout Frequent occurrence of turbd water due to extreme events Typhoon Rusa(2002), Meam(2003) Dscharge release lmtaton due to hgh turbdty Optmzed reservor operaton for hgh turbd water Flood season: Release through selectve wthdrawal faclty to prevent turbdty spread and overturnng Dry season: Jont dam operaton to mantan the downstream turbdty less than 30 NTU Turbd water management Watershed assessment Identfy hgh turbdty layer Adequate Dscharge release When / how much Impact on reservor downstream Turbd Water Intruson & Overturnng Impact on Downstream

3 Problems Stop reservor operaton Hnders water treatment plant operaton Negatve Impact on Downstream

4 Introducton Focused on watershed management for turbd water control Assessment on the mpact of dfferent spatal data qualty and the effcency of SWAT model for runoff and suspended sedment Frst, the mpacts of fve DEM grd szes (.e. 30m, 60m, 90m, 120m, and 150m) on model nputs (parameters) and outputs Combnaton of 30m and 120m DEMs wth two dfferent scales of land cover and sol type map

5 Imha & Andong Watershed Propertes Imha Andong Table 1. comparson of two catchments Area (km 2 ) 1,361 1,584 Major Landcover Forest (79.8%) Agrculture (15.0%) Forest (82.2%) Agrculture (11.9%) Major Sol type Ms (Lthosols, Mcaceous and Hard Slceous Materals,slty loam or sandy loam) Ma (Lthosols, Slceous Crystallne Materals, slty loam or sandy loam) Avg. Ann. Precptaton (mm, ) Total channel length (km) 1,158 1,348 5,741 5,381 Storage volume (m 3 ) 424M 1000M Elevaton (m, EL) 54~1, ~1,550

6 Ste descrpton

7 5 dfferent scaled DEM

8 DEM wth General Watershed Parameters Grd sze (m) AREA (Km 2 ) SLOPE (m/m) SLOPEL (m) CHSLOPE (m/m) CHWID (m) CHLEN (Km) D30 1, D60 1, D90 1, D120 1, D150 1, DEM scale Increases (low DEM Resoluton) Slope decrease Slope Length ncrease Channel slope ncrease Channel wdth decrease Channel length decrease

9 DEM wth Water and Sedment Grd sze (m) SUR (mm) LAT (mm) GW (mm) WY (mm) SYL (ton/ha) D D D D D When DEM scale Increases (Coarser DEM Resoluton) Surface runoff ncrease Lateral flow decrease Ground water ncrease Net water yeld ncrease Sedment yelds ncrease

10 CV wth DEM resoluton Slope length, Lateral flow, subsurface flow, and sedment yeld are more senstve to DEM resoluton Coeffcents of Varaton Area Slope Slope length Channel slope Channel wdth Channel length Surface flow Lateral flow Grounwater Water yeld Sedment yeld Input Output

11 Land Cover maps 1:50,000 and 1:25,000

12 1:50,000 Landcover map - total 8 classes of landuse Class Area (%) Class Area (%) Class Area (%) Class Area (%) Urban 0.88 Wetland 10.1 Forest 0.88 Farm feld 10.1 Follow 0.02 Pasture 0.03 Rce 0.02 Water :25,000 Landcover map 18 classes of land use Class Area (%) Class Area (%) Class Area (%) Resdence 0.88 Paddy 3.34 Decduous forest Industry 0.02 Farm feld 10.1 Evergreen forest Commercal 0.05 Vnyl house 0.03 Mxed forest Recreaton 0.00 Orchard 1.43 Pasture 0.40 Transportaton 0.34 Other crops 0.07 Wetland 0.45 Insttutonal 0.08 Fallow 0.61 Water 2.4

13 Sol Maps 1:250,000 and 1:50,000

14 Sol types and descrpton 11 sol types for 1:250,000 sol map, whle 32 sol types for and 1:50,000 Sol types Af An Ap Ma Mm Ms Mu Mv Ra Rocky Rs Descrptons alluval sols and rver wash, flood plans complex of sols, narrow valleys low-humc gley and alluval sols, alluval plans ltho sols, slceous crstallne materals ltho sols, mcaceous and hard slceous materals ltho sols, sedmentary materals brown forest sols and lthosols, undfferentated materals ltho sols, slceomafc materals red-yellow podzolc sols slceous crystallne materals rocky lands ltho sols and red-yellow podzolc sols, sedmentary materals

15 DEM wth Water and Sedment Grd sze (m) SUR (mm) LAT (mm) GW (mm) WY (mm) SYL (ton/ha) D D D D D When DEM scale Increases (Coarser DEM Resoluton) Surface runoff ncrease Lateral flow decrease Ground water ncrease Net water yeld ncrease Sedment yelds ncrease

16 Parameter varaton wth sol map scale Scale SOL_Z mm SOL_BD g/cm 3 SOL_AWC mm/mm SOL_K mm/hr USLE_K HRU SB(1:250,000) SM(1:50,000) ,567 Name SOL_Z Defnton Depth from sol surface to bottom of layer SOL_BD Most bulk densty (cm -3 ) SOL_AWC SOL_K USLE_K HRU Avalable water capacty of the sol layer (mm/mm sol) Saturated hydraulc conductvty (mm/h) USLE equaton sol erodblty (K) factor Multple hydrologc response unt number

17 Predcton of daly flow usng SWAT Cheongsong Precptaton Observed 100 (m 3 /s) Smulated P (mm/day) /4/1 2003/6/1 2003/8/1 2003/10/1 2003/12/1 Tme (date) 400 Youngyang Precptaton Observed 100 (m 3 /s) Smulated P (mm/day) /4/1 2004/6/1 2004/8/1 2004/10/1 2004/12/1 Tme (date) 400

18 Predcton of daly Suspended Sedment usng SWAT Cheongsong Youngyang

19 Statstcal ndexes for runoff computaton 2,, 2,, ) ( ) ( 1 = n obs obs n pred obs eff R = n obs n pred obs E V ) ( ) (,,, = n pred obs N RMSE 2,, ) ( 1 ) )( ( ) ( ,, 2 = n pred pred n obs obs n pred obs pred obs n n n R

20 Model effcency for runoff Scenaro DEMs Land cover Sol Scenaro DEMs Land cover Sol Case 1 30 m 1:50,000 1:250,000 Case m 1:50,000 1:250,000 Case 2 30 m 1:50,000 1:50,000 Case m 1:50,000 1:50,000 Case 3 30 m 1:25,000 1:250,000 Case m 1:25,000 1:250,000 Case 4 30 m 1:25,000 1:50,000 Case m 1:25,000 1:50, Effcency Value Reff R2 VE RMSE case-1 case-2 case-3 case-4 case-5 case-6 case-7 case Effcency Value (RMSE) Effcency Value Reff R2 VE RMSE case-1 case-2 case-3 case-4 case-5 case-6 case-7 case Effcency Value (RMSE)

21 Model effcency for suspended sedment Only R2 was used for model assessment Effcency value (R 2 ) case-1 case-2 case-3 case-4 case-5 case-6 case-7 case-8

22 Dscusson Wth fner DEM, model effcency mproved for runoff smulaton Wth coarser scaled Land use, model effcency decreases at Yongyang but ncreases at Chongsong staton Coarser scaled sol map mproves model effcency only for Ve, whle other statstcal ndexes showed no sgnfcant varaton For sedment, model effcency decreases wth coarser DEM Land use and sol maps dd not show any effects on the mprovement of suspended sedment Should fnd more consstent gudelnes n selecton of nput data resoluton

23 Thank You!!

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

SEDIMENT TRANSPORT PROCESSES IN MOUNTAIN AREA OF KINUGAWA RIVER

SEDIMENT TRANSPORT PROCESSES IN MOUNTAIN AREA OF KINUGAWA RIVER SEDIMENT TRANSPORT PROCESSES IN MOUNTAIN AREA OF KINUGAWA RIVER Catherne G. Jaceldone 1 Supervsors: Atsuhro Yorozuya 2 MEE15625 Shnj Egashra ABSTRACT Ths study ams to develop a sedment transport model

More information

Developing regional flood frequency models for Victoria, Australia by using Index Flood (IF) approach

Developing regional flood frequency models for Victoria, Australia by using Index Flood (IF) approach 18 th World IMACS / MODSIM Congress, Carns, Australa 13-17 July 2009 http://mssanz.org.au/modsm09 Developng regonal flood frequency models for Vctora, Australa by usng Index Flood (IF) Masan, M. N 1. and

More information

DISSERTATION NUMERICAL MODELING OF RESERVOIR SEDIMENTATION AND FLUSHING PROCESSES. Submitted by. Jungkyu Ahn

DISSERTATION NUMERICAL MODELING OF RESERVOIR SEDIMENTATION AND FLUSHING PROCESSES. Submitted by. Jungkyu Ahn DISSERTATION NUMERICAL MODELING OF RESERVOIR SEDIMENTATION AND FLUSHING PROCESSES Submtted by Jungkyu Ahn Department of Cvl and Envronmental Engneerng In partal fulfllment of the requrements For the Degree

More information

Estimation Of Long-Term Sediment Loads In The Fitzroy Catchment, Queensland, Australia

Estimation Of Long-Term Sediment Loads In The Fitzroy Catchment, Queensland, Australia Estmaton Of Long-Term Sedment Loads In The Ftzroy Catchment, Queensland, Australa Joo, M., 2 B. Yu, B. Fente and 3 C. Caroll Natural Resources and Mnes, 80 Meers Rd Indoorooplly Qld 4068 Emal: Maranna.Joo@nrm.qld.gov.au

More information

Short Term Load Forecasting using an Artificial Neural Network

Short Term Load Forecasting using an Artificial Neural Network Short Term Load Forecastng usng an Artfcal Neural Network D. Kown 1, M. Km 1, C. Hong 1,, S. Cho 2 1 Department of Computer Scence, Sangmyung Unversty, Seoul, Korea 2 Department of Energy Grd, Sangmyung

More information

Use Subsurface Altitude and Groundwater Level Variations to Estimate Land Subsidence in Choushui River Alluvial Fan, Taiwan. Reporter : Ya-Yun Zheng

Use Subsurface Altitude and Groundwater Level Variations to Estimate Land Subsidence in Choushui River Alluvial Fan, Taiwan. Reporter : Ya-Yun Zheng Use Subsurface Alttude and Groundwater Level Varatons to Estmate Land Subsdence n Choushu Rver Alluval Fan, Tawan 1 Reporter : Ya-Yun Zheng Graduate School of Safety Health and Envronmental Engneerng,

More information

Earth-surface Dynamics Modeling & Model Coupling

Earth-surface Dynamics Modeling & Model Coupling Earth-surface Dynamcs Modelng & Model Couplng A short course James PM Syvtsk & Erc WH Hutton, CSDMS, CU-Boulder Wth specal thanks to Pat Wberg, Carl Fredrchs, Courtney Harrs, Chrs Reed, Rocky Geyer, Alan

More information

Study on Effect of Different Porosity on Thermally Driven Heat Transfer in a Centrifugal Force Field

Study on Effect of Different Porosity on Thermally Driven Heat Transfer in a Centrifugal Force Field 5th Internatonal Conference on Cvl Engneerng and Transportaton (ICCET 015) Study on Effect of Dfferent Porosty on Thermally Drven Heat Transfer n a Centrfugal Force Feld 1 Xa Je1,a*, Chang Ha-png,b, and

More information

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour Geodetc Week 00 October 05-07, Cologne S4: Appled Geodesy and GNSS A correcton model for zenth dry delay of GPS sgnals usng regonal meteorologcal stes Xaoguang Luo Geodetc Insttute, Department of Cvl Engneerng,

More information

Estimating Spatial Sediment Delivery Ratio on a Large Rural Catchment

Estimating Spatial Sediment Delivery Ratio on a Large Rural Catchment Journal of Spatal Hydrology Vol.6, No.1 Sprng 2006 Estmatng Spatal Sedment Delvery Rato on a Large Rural Catchment Benedct M. Mutua 1, 2 and Andreas Klk 1 Abstract Sol eroson and sedment yeld from catchments

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

Spatial Statistics and Analysis Methods (for GEOG 104 class).

Spatial Statistics and Analysis Methods (for GEOG 104 class). Spatal Statstcs and Analyss Methods (for GEOG 104 class). Provded by Dr. An L, San Dego State Unversty. 1 Ponts Types of spatal data Pont pattern analyss (PPA; such as nearest neghbor dstance, quadrat

More information

Development of a Semi-Automated Approach for Regional Corrector Surface Modeling in GPS-Levelling

Development of a Semi-Automated Approach for Regional Corrector Surface Modeling in GPS-Levelling Development of a Sem-Automated Approach for Regonal Corrector Surface Modelng n GPS-Levellng G. Fotopoulos, C. Kotsaks, M.G. Sders, and N. El-Shemy Presented at the Annual Canadan Geophyscal Unon Meetng

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation

Statistics for Managers Using Microsoft Excel/SPSS Chapter 13 The Simple Linear Regression Model and Correlation Statstcs for Managers Usng Mcrosoft Excel/SPSS Chapter 13 The Smple Lnear Regresson Model and Correlaton 1999 Prentce-Hall, Inc. Chap. 13-1 Chapter Topcs Types of Regresson Models Determnng the Smple Lnear

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VIII LECTURE - 34 ANALYSIS OF VARIANCE IN RANDOM-EFFECTS MODEL AND MIXED-EFFECTS EFFECTS MODEL Dr Shalabh Department of Mathematcs and Statstcs Indan

More information

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

More information

Outline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property.

Outline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property. Unt Eght Calculatons wth Entropy Mechancal Engneerng 370 Thermodynamcs Larry Caretto October 6, 010 Outlne Quz Seven Solutons Second law revew Goals for unt eght Usng entropy to calculate the maxmum work

More information

NUMERICAL STUDY ON FLUSHING CHANNEL EVOLUTION, CASE STUDY OF DASHIDAIRA RESERVOIR, KUROBE RIVER

NUMERICAL STUDY ON FLUSHING CHANNEL EVOLUTION, CASE STUDY OF DASHIDAIRA RESERVOIR, KUROBE RIVER A n n u a l Jo u r n a l o f Hy d rau lc En g n e e rn g, JS CE, V o l. 59, 01 5, F e b ru a ry NUMERICAL STUDY ON FLUSHING CHANNEL EVOLUTION, CASE STUDY OF DASHIDAIRA RESERVOIR, KUROBE RIVER Taymaz ESMAEILI1,

More information

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X).

where I = (n x n) diagonal identity matrix with diagonal elements = 1 and off-diagonal elements = 0; and σ 2 e = variance of (Y X). 11.4.1 Estmaton of Multple Regresson Coeffcents In multple lnear regresson, we essentally solve n equatons for the p unnown parameters. hus n must e equal to or greater than p and n practce n should e

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

Application of Set Pair Analysis on QPE and Rain Gauge in Flood Forecasting Zhiyuan Yin1,a, Fang Yang2,b, Tieyuan Shen1,c

Application of Set Pair Analysis on QPE and Rain Gauge in Flood Forecasting Zhiyuan Yin1,a, Fang Yang2,b, Tieyuan Shen1,c Advances n Engneerng Research (AER), volume 24 2nd Internatonal Symposum on Advances n Electrcal, Electroncs and Computer Engneerng (ISAEECE 27) Applcaton of Set Par Analyss on QPE and Ran Gauge n Flood

More information

STREAM FLOW MODELING IN THE NACUNDAY RIVER BASIN (PARAGUAY, SOUTH AMERICA) USING SWAT MODEL. Sandra Mongelos and Manoj K. Jain

STREAM FLOW MODELING IN THE NACUNDAY RIVER BASIN (PARAGUAY, SOUTH AMERICA) USING SWAT MODEL. Sandra Mongelos and Manoj K. Jain STREAM FLOW MODELING IN THE NACUNDAY RIVER BASIN (PARAGUAY, SOUTH AMERICA) USING SWAT MODEL Sandra Mongelos and Manoj K. Jain DEPARTMENT OF HYDROLOGY INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE 247

More information

and Statistical Mechanics Material Properties

and Statistical Mechanics Material Properties Statstcal Mechancs and Materal Propertes By Kuno TAKAHASHI Tokyo Insttute of Technology, Tokyo 15-855, JAPA Phone/Fax +81-3-5734-3915 takahak@de.ttech.ac.jp http://www.de.ttech.ac.jp/~kt-lab/ Only for

More information

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors Multple Lnear and Polynomal Regresson wth Statstcal Analyss Gven a set of data of measured (or observed) values of a dependent varable: y versus n ndependent varables x 1, x, x n, multple lnear regresson

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

A strong wind prediction model for the railway operation based on the onsite measurement and the numerical simulation

A strong wind prediction model for the railway operation based on the onsite measurement and the numerical simulation A strong wnd predcton for the ralway operaton based on the onste measurement and the numercal smulaton Yayo Msu a, Atsush Yamaguch b, and Takesh Ishhara c a Dsaster Preventon Research Laboratory, East

More information

International Power, Electronics and Materials Engineering Conference (IPEMEC 2015)

International Power, Electronics and Materials Engineering Conference (IPEMEC 2015) Internatonal Power, Electroncs and Materals Engneerng Conference (IPEMEC 2015) Dynamc Model of Wnd Speed Dstrbuton n Wnd Farm Consderng the Impact of Wnd Drecton and Interference Effects Zhe Dong 1, a,

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist?

UNR Joint Economics Working Paper Series Working Paper No Further Analysis of the Zipf Law: Does the Rank-Size Rule Really Exist? UNR Jont Economcs Workng Paper Seres Workng Paper No. 08-005 Further Analyss of the Zpf Law: Does the Rank-Sze Rule Really Exst? Fungsa Nota and Shunfeng Song Department of Economcs /030 Unversty of Nevada,

More information

Three-Phase Distillation in Packed Towers: Short-Cut Modelling and Parameter Tuning

Three-Phase Distillation in Packed Towers: Short-Cut Modelling and Parameter Tuning European Symposum on Computer Arded Aded Process Engneerng 15 L. Pugjaner and A. Espuña (Edtors) 2005 Elsever Scence B.V. All rghts reserved. Three-Phase Dstllaton n Packed Towers: Short-Cut Modellng and

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Thermo-Calc Software. Modelling Multicomponent Precipitation Kinetics with CALPHAD-Based Tools. EUROMAT2013, September 8-13, 2013 Sevilla, Spain

Thermo-Calc Software. Modelling Multicomponent Precipitation Kinetics with CALPHAD-Based Tools. EUROMAT2013, September 8-13, 2013 Sevilla, Spain Modellng Multcomponent Precptaton Knetcs wth CALPHAD-Based Tools Kasheng Wu 1, Gustaf Sterner 2, Qng Chen 2, Åke Jansson 2, Paul Mason 1, Johan Bratberg 2 and Anders Engström 2 1 Inc., 2 AB EUROMAT2013,

More information

DYNAMIC MODEL OF GULLY EROSION

DYNAMIC MODEL OF GULLY EROSION DYNAMIC MODEL OF GULLY EROSION A.Sdorchuk Lab.of Sol Eroson and Fluval Processes, Geographcal Faculty,Moscow State Unversty, Moscow, 119899, Russa, ph.:7 095 9395697, fax:7 095 938836, Emal:sdor@yas.geogr.msu.su

More information

The economics of climate change impacts on agriculture: ongoing work using Ricardian approaches

The economics of climate change impacts on agriculture: ongoing work using Ricardian approaches The economcs of clmate change mpacts on agrculture: ongong work usng Rcardan approaches Emanuele Massett Fondazone En Enrco Matte - FEEM Euro-Medterranean Centre for Clmate Change - CMCC Jont work wth

More information

Optimization of the thermodynamic model of a solar driven Aqua - ammonia absorption refrigeration system

Optimization of the thermodynamic model of a solar driven Aqua - ammonia absorption refrigeration system nd WSEAS/IASME Internatonal Conference on RENEWABLE ENERGY SOURCES (RES') Corfu, Greece, October -, Optmzaton of the thermodynamc model of a solar drven Aqua - ammona absorpton refrgeraton system J. ABDULATEEF,

More information

Multiple Sound Source Location in 3D Space with a Synchronized Neural System

Multiple Sound Source Location in 3D Space with a Synchronized Neural System Multple Sound Source Locaton n D Space wth a Synchronzed Neural System Yum Takzawa and Atsush Fukasawa Insttute of Statstcal Mathematcs Research Organzaton of Informaton and Systems 0- Mdor-cho, Tachkawa,

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

STATISTICAL DOWNSCALING OF PRECIPITATION WITH A FORMATTED REGRESSION FRAME

STATISTICAL DOWNSCALING OF PRECIPITATION WITH A FORMATTED REGRESSION FRAME Annual Journal of Hydraulc Engneerng, JSCE, Vol.58, 2014, February STATISTICAL DOWNSCALING OF PRECIPITATION WITH A FORMATTED REGRESSION FRAME Sunmn KIM 1, Ech NAKAKITA 2, Yasuto TACHIKAWA 3, Mchharu SHIIBA

More information

Statistics MINITAB - Lab 2

Statistics MINITAB - Lab 2 Statstcs 20080 MINITAB - Lab 2 1. Smple Lnear Regresson In smple lnear regresson we attempt to model a lnear relatonshp between two varables wth a straght lne and make statstcal nferences concernng that

More information

Air Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong

Air Age Equation Parameterized by Ventilation Grouped Time WU Wen-zhong Appled Mechancs and Materals Submtted: 2014-05-07 ISSN: 1662-7482, Vols. 587-589, pp 449-452 Accepted: 2014-05-10 do:10.4028/www.scentfc.net/amm.587-589.449 Onlne: 2014-07-04 2014 Trans Tech Publcatons,

More information

REAL-TIME DETERMINATION OF INDOOR CONTAMINANT SOURCE LOCATION AND STRENGTH, PART II: WITH TWO SENSORS. Beijing , China,

REAL-TIME DETERMINATION OF INDOOR CONTAMINANT SOURCE LOCATION AND STRENGTH, PART II: WITH TWO SENSORS. Beijing , China, REAL-TIME DETERMIATIO OF IDOOR COTAMIAT SOURCE LOCATIO AD STREGTH, PART II: WITH TWO SESORS Hao Ca,, Xantng L, Wedng Long 3 Department of Buldng Scence, School of Archtecture, Tsnghua Unversty Bejng 84,

More information

APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS

APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS 6th ICPT, Sapporo, Japan, July 008 APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS James MAINA Prncpal Researcher, Transport and Infrastructure Engneerng, CSIR Bult Envronment

More information

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning

Variability-Driven Module Selection with Joint Design Time Optimization and Post-Silicon Tuning Asa and South Pacfc Desgn Automaton Conference 2008 Varablty-Drven Module Selecton wth Jont Desgn Tme Optmzaton and Post-Slcon Tunng Feng Wang, Xaoxa Wu, Yuan Xe The Pennsylvana State Unversty Department

More information

29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies

29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies TESTING THE SPECTRAL DECONVOLUTION ALGORITHM TOOL (SDAT) WITH XE SPECTRA Steven R. Begalsk, Kendra M. Foltz Begalsk, and Derek A. Haas The Unversty of Texas at Austn Sponsored by Army Space and Mssle Defense

More information

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013 INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013 Copyrght by the authors - Lcensee IPA- Under Creatve Commons lcense 3.0 Research artcle ISSN 0976 4399 Sudarshan patowary

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

3-D NUMERICAL SIMULATION OF FLOW AND CLEAR WATER SCOUR BY INTERACTION BETWEEN BRIDGE PIERS

3-D NUMERICAL SIMULATION OF FLOW AND CLEAR WATER SCOUR BY INTERACTION BETWEEN BRIDGE PIERS Tenth Internatonal Water Technology Conference, IWTC10 2006, Aleandra, Egypt 899 3-D NMERICAL SIMLATION OF FLOW AND CLEAR WATER SCOR BY INTERACTION BETWEEN BRIDGE PIERS Gamal A. A. Abouzed, Hassan I. Mohamed

More information

Basic Business Statistics, 10/e

Basic Business Statistics, 10/e Chapter 13 13-1 Basc Busness Statstcs 11 th Edton Chapter 13 Smple Lnear Regresson Basc Busness Statstcs, 11e 009 Prentce-Hall, Inc. Chap 13-1 Learnng Objectves In ths chapter, you learn: How to use regresson

More information

Design and Manufacturing Engineering Department, Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia 2

Design and Manufacturing Engineering Department, Politeknik Perkapalan Negeri Surabaya, Surabaya, Indonesia 2 OPTIMIZATION OF MULTIPLE PERFORMANCE CHARACTERISTICS IN WIRE ELECTRICAL DISCHARGE MACHINING (WEDM) PROCESS OF BUDERUS 28 TOOL STEEL USING TAGUCHI-GREY- FUZZY METHOD D. A. Purnomo 1,2, B. O. P. Soepangkat

More information

STUDIES ON RIVERBED EROSION ON THE RABA RIVER CLOSE TO THE STRÓ A GAUGING STATION

STUDIES ON RIVERBED EROSION ON THE RABA RIVER CLOSE TO THE STRÓ A GAUGING STATION INFRASTRUKTURA I EKOLOGIA TERENÓW WIEJSKICH INFRASTRUCTURE AND ECOLOGY OF RURAL AREAS Studes on rverbed... Nr /0, POLSKA AKADEMIA NAUK, Oddza w Krakowe, s. 79 9 Komsja Techncznej Infrastruktury Ws Commsson

More information

Evaluation of the SWAT Model Setup Process Through A Case Study in Roxo Catchment, Portugal

Evaluation of the SWAT Model Setup Process Through A Case Study in Roxo Catchment, Portugal Evaluation of the SWAT Model Setup Process Through A Case Study in Roxo Catchment, Portugal Mustafa Gökmen Master Degree in on Geo-information and Earth Observation for Integrated Catchment and Water Resources

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

Simulation of Flow Pattern in Open Channels with Sudden Expansions

Simulation of Flow Pattern in Open Channels with Sudden Expansions Research Journal of Appled Scences, Engneerng and Technology 4(19): 3852-3857, 2012 ISSN: 2040-7467 Maxwell Scentfc Organzaton, 2012 Submtted: May 11, 2012 Accepted: June 01, 2012 Publshed: October 01,

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

8. Modelling Uncertainty

8. Modelling Uncertainty 8. Modellng Uncertanty. Introducton. Generatng Values From Known Probablty Dstrbutons. Monte Carlo Smulaton 4. Chance Constraned Models 5 5. Markov Processes and Transton Probabltes 6 6. Stochastc Optmzaton

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

General displacement arch-cantilever element method for stress analysis of arch dam

General displacement arch-cantilever element method for stress analysis of arch dam Water Scence and Engneerng, 009, (): 3-4 do:0.388/j.ssn.674-370.009.0.004 http://kkb.hhu.edu.cn e-mal: wse@hhu.edu.cn General dsplacement arch-cantlever element method for stress analyss of arch dam Hao

More information

Testing for seasonal unit roots in heterogeneous panels

Testing for seasonal unit roots in heterogeneous panels Testng for seasonal unt roots n heterogeneous panels Jesus Otero * Facultad de Economía Unversdad del Rosaro, Colomba Jeremy Smth Department of Economcs Unversty of arwck Monca Gulett Aston Busness School

More information

CFD VALIDATION OF STRATIFIED TWO-PHASE FLOWS IN A HORIZONTAL CHANNEL

CFD VALIDATION OF STRATIFIED TWO-PHASE FLOWS IN A HORIZONTAL CHANNEL CFD VALIDATION OF STRATIFIED TWO-PHASE FLOWS IN A HORIZONTAL CHANNEL 1. Introducton Chrstophe Vallée and Thomas Höhne In dfferent scenaros of small break Loss of Coolant Accdent (SB-LOCA), stratfed twophase

More information

Watershed Processes and Modeling

Watershed Processes and Modeling Watershed Processes and Modeling Pierre Y. Julien Hyeonsik Kim Department of Civil Engineering Colorado State University Fort Collins, Colorado Kuala Lumpur - May Objectives Brief overview of Watershed

More information

Aerosol forecast verification at ECMWF

Aerosol forecast verification at ECMWF Aerosol forecast verfcaton at ECMWF Luke Jones Angela Benedett ECMWF Acknowledgements: Jean-Jacques Morcrette and Johannes Kaser Quck overvew of the MACC/ECMWF aerosol analyss and forecastng system Forward

More information

Bi-Monthly. Electronic International Interdisciplinary Research Journal (EIIRJ) REVIEWED INTERNATIONAL JOURNAL. ISSN: Impact factor: 2.

Bi-Monthly. Electronic International Interdisciplinary Research Journal (EIIRJ) REVIEWED INTERNATIONAL JOURNAL. ISSN: Impact factor: 2. 015 Electronc Internatonal Interdscplnary Research Journal (EIIRJ) REVIEWED IERAIOAL JOURAL : 77-871 Impact factor:.085 B-Monthly VOL - IV ISSUES - V Chef-Edtor: Ubale Amol Baban COMPARISO OF RAIFALL ESIMAES

More information

Time-Varying Coefficient Model with Linear Smoothing Function for Longitudinal Data in Clinical Trial

Time-Varying Coefficient Model with Linear Smoothing Function for Longitudinal Data in Clinical Trial Tme-Varyng Coeffcent Model wth Lnear Smoothng Functon for Longtudnal Data n Clncal Tral Masanor Ito, Toshhro Msum and Hdek Hrooka Bostatstcs Group, Data Scence Dept., Astellas Pharma Inc. Introducton In

More information

Continuous vs. Discrete Goods

Continuous vs. Discrete Goods CE 651 Transportaton Economcs Charsma Choudhury Lecture 3-4 Analyss of Demand Contnuous vs. Dscrete Goods Contnuous Goods Dscrete Goods x auto 1 Indfference u curves 3 u u 1 x 1 0 1 bus Outlne Data Modelng

More information

HEAT TRANSFER IN ELECTRONIC PACKAGE SUBMITTED TO TIME-DEPENDANT CLIMATIC CONDITIONS

HEAT TRANSFER IN ELECTRONIC PACKAGE SUBMITTED TO TIME-DEPENDANT CLIMATIC CONDITIONS ISTP-16, 005, PRAGUE 16 TH INTERNATIONAL SYMPOSIUM ON TRANSPORT PHENOMENA HEAT TRANSFER IN ELECTRONIC PACKAGE SUBMITTED TO TIME-DEPENDANT CLIMATIC CONDITIONS Valére Ménard*, Phlppe Reulet**, Davd Nörtershäuser*,

More information

#64. ΔS for Isothermal Mixing of Ideal Gases

#64. ΔS for Isothermal Mixing of Ideal Gases #64 Carnot Heat Engne ΔS for Isothermal Mxng of Ideal Gases ds = S dt + S T V V S = P V T T V PV = nrt, P T ds = v T = nr V dv V nr V V = nrln V V = - nrln V V ΔS ΔS ΔS for Isothermal Mxng for Ideal Gases

More information

Y = β 0 + β 1 X 1 + β 2 X β k X k + ε

Y = β 0 + β 1 X 1 + β 2 X β k X k + ε Chapter 3 Secton 3.1 Model Assumptons: Multple Regresson Model Predcton Equaton Std. Devaton of Error Correlaton Matrx Smple Lnear Regresson: 1.) Lnearty.) Constant Varance 3.) Independent Errors 4.) Normalty

More information

Relevant polarimetric parameters for surface characterization using SAR data

Relevant polarimetric parameters for surface characterization using SAR data Relevant polarmetrc parameters for surface characterzaton usng SAR data INTRODUCTION S. Allan, L. Ferro-Faml, E. Potter Unversty of Rennes I.E.T.R, UMR CNRS 664, Image and Remote Sensng Group Campus de

More information

Chapter 4. Velocity analysis

Chapter 4. Velocity analysis 1 Chapter 4 Velocty analyss Introducton The objectve of velocty analyss s to determne the sesmc veloctes of layers n the subsurface. Sesmc veloctes are used n many processng and nterpretaton stages such

More information

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp.

ORIGIN 1. PTC_CE_BSD_3.2_us_mp.mcdx. Mathcad Enabled Content 2011 Knovel Corp. Clck to Vew Mathcad Document 2011 Knovel Corp. Buldng Structural Desgn. homas P. Magner, P.E. 2011 Parametrc echnology Corp. Chapter 3: Renforced Concrete Slabs and Beams 3.2 Renforced Concrete Beams -

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Recall: man dea of lnear regresson Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 8 Lnear regresson can be used to study an

More information

Transient Stability Assessment of Power System Based on Support Vector Machine

Transient Stability Assessment of Power System Based on Support Vector Machine ransent Stablty Assessment of Power System Based on Support Vector Machne Shengyong Ye Yongkang Zheng Qngquan Qan School of Electrcal Engneerng, Southwest Jaotong Unversty, Chengdu 610031, P. R. Chna Abstract

More information

Parking Demand Forecasting in Airport Ground Transportation System: Case Study in Hongqiao Airport

Parking Demand Forecasting in Airport Ground Transportation System: Case Study in Hongqiao Airport Internatonal Symposum on Computers & Informatcs (ISCI 25) Parkng Demand Forecastng n Arport Ground Transportaton System: Case Study n Hongqao Arport Ln Chang, a, L Wefeng, b*, Huanh Yan 2, c, Yang Ge,

More information

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding

Lecture 9: Linear regression: centering, hypothesis testing, multiple covariates, and confounding Lecture 9: Lnear regresson: centerng, hypothess testng, multple covarates, and confoundng Sandy Eckel seckel@jhsph.edu 6 May 008 Recall: man dea of lnear regresson Lnear regresson can be used to study

More information

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression 11 MACHINE APPLIED MACHINE LEARNING LEARNING MACHINE LEARNING Gaussan Mture Regresson 22 MACHINE APPLIED MACHINE LEARNING LEARNING Bref summary of last week s lecture 33 MACHINE APPLIED MACHINE LEARNING

More information

A Bound for the Relative Bias of the Design Effect

A Bound for the Relative Bias of the Design Effect A Bound for the Relatve Bas of the Desgn Effect Alberto Padlla Banco de Méxco Abstract Desgn effects are typcally used to compute sample szes or standard errors from complex surveys. In ths paper, we show

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Boise State University Department of Electrical and Computer Engineering ECE 212L Circuit Analysis and Design Lab

Boise State University Department of Electrical and Computer Engineering ECE 212L Circuit Analysis and Design Lab Bose State Unersty Department of Electrcal and omputer Engneerng EE 1L rcut Analyss and Desgn Lab Experment #8: The Integratng and Dfferentatng Op-Amp rcuts 1 Objectes The objectes of ths laboratory experment

More information

En Route Traffic Optimization to Reduce Environmental Impact

En Route Traffic Optimization to Reduce Environmental Impact En Route Traffc Optmzaton to Reduce Envronmental Impact John-Paul Clarke Assocate Professor of Aerospace Engneerng Drector of the Ar Transportaton Laboratory Georga Insttute of Technology Outlne 1. Introducton

More information

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore Sesson Outlne Introducton to classfcaton problems and dscrete choce models. Introducton to Logstcs Regresson. Logstc functon and Logt functon. Maxmum Lkelhood Estmator (MLE) for estmaton of LR parameters.

More information

Numerical Study of Propane-Air Mixture Combustion in a Burner Element

Numerical Study of Propane-Air Mixture Combustion in a Burner Element Defect and Dffuson Forum Vols. 73-76 (8 pp 144-149 onlne at http://www.scentfc.net (8 Trans Tech Publcatons, Swtzerland Onlne avalable snce 8/Feb/11 Numercal Study of Propane-Ar Mxture Combuston n a Burner

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Adjusted Control Lmts for U Charts Copyrght 207 by Taylor Enterprses, Inc., All Rghts Reserved. Adjusted Control Lmts for U Charts Dr. Wayne A. Taylor Abstract: U charts are used

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

Amiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business

Amiri s Supply Chain Model. System Engineering b Department of Mathematics and Statistics c Odette School of Business Amr s Supply Chan Model by S. Ashtab a,, R.J. Caron b E. Selvarajah c a Department of Industral Manufacturng System Engneerng b Department of Mathematcs Statstcs c Odette School of Busness Unversty of

More information

[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students.

[The following data appear in Wooldridge Q2.3.] The table below contains the ACT score and college GPA for eight college students. PPOL 59-3 Problem Set Exercses n Smple Regresson Due n class /8/7 In ths problem set, you are asked to compute varous statstcs by hand to gve you a better sense of the mechancs of the Pearson correlaton

More information

Modeling the Effects of Climate and Land Cover Change in the Stoney Brook Subbasin of the St. Louis River Watershed

Modeling the Effects of Climate and Land Cover Change in the Stoney Brook Subbasin of the St. Louis River Watershed Modeling the Effects of Climate and Land Cover Change in the Stoney Brook Subbasin of the St. Louis River Watershed Joe Johnson and Jesse Pruette 214 NASA Research Internship Geospatial Technologies Program

More information

Application research on rough set -neural network in the fault diagnosis system of ball mill

Application research on rough set -neural network in the fault diagnosis system of ball mill Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(4):834-838 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Applcaton research on rough set -neural network n the

More information

Application of SWAT Model to Estimate the Runoff and Sediment Load from the Right Bank Valleys of Mosul Dam Reservoir

Application of SWAT Model to Estimate the Runoff and Sediment Load from the Right Bank Valleys of Mosul Dam Reservoir Application of SWAT Model to Estimate the Runoff and Sediment Load from the Right Bank Valleys of Mosul Dam Reservoir Dr Mohammad Ezeel Deen Prof. Nadhir Al-Ansari Prof Sven Knutsson Figure 1.Map of Iraq

More information

Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT

Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT Hydrologic Modelling of the Upper Malaprabha Catchment using ArcView SWAT Technical briefs are short summaries of the models used in the project aimed at nontechnical readers. The aim of the PES India

More information

Geotechnical Engineering Journal of the SEAGS & AGSSEA Vol. 47 No. 1 March 2016 ISSN

Geotechnical Engineering Journal of the SEAGS & AGSSEA Vol. 47 No. 1 March 2016 ISSN Effect of Vacuum Pressure Dstrbuton on Settlement Analyss Results for An Improved Thck Soft Clay Depost at Sa Gon-Hep Phuoc Termnal Port, South of Vetnam Hoang Hep 1 and Pham Huy Gao 2* 1 Portcoast Consultant

More information

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances ec Annexes Ths Annex frst llustrates a cycle-based move n the dynamc-block generaton tabu search. It then dsplays the characterstcs of the nstance sets, followed by detaled results of the parametercalbraton

More information

ITTC - Recommended Procedures and Guidelines

ITTC - Recommended Procedures and Guidelines Page of 9 able of Contents UCERAIY AALYSIS - EXAMPLE FOR WAERJE PROPULSIO ES... 2. PURPOSE OF PROCEDURE... 2 2. EXAMPLE FOR WAERJE PROPULSIO ES... 2 2. est Desgn... 2 2.2 Measurement Systems and Procedure...

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information