Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : SWG

Climatic Change Scenario(2007-2037) For TuzKhormatoo Region

Cheleng A.Arselan

Engineering and Technology Journal, 2010, Volume 28, Issue 22, Pages 6496-6505

The estimation of minimum temperature, maximum temperature,
humidity and precipitation and all other climatic variables needs a range of models depending on the time scales involved. In this research a comprehensive models to generate minimum, maximum temperature and humidity based on monthly mean values for TuzKhormatoo region were developed for 30 years ahead .All these models were depended on a previous monthly data which were documented for the period (10/1978-12/2009). The Stochastic weather generator (SWG) models were used to compute the climatic variables by adjusting the parameters appropriately for the future climates factors and then by using them to
estimate maximum, minimum temperature and humidity .It was concluded from this research that there will be an increase in the monthly mean values of the maximum and minimum temperature values of this region in the future. It was concluded also due to the generation process that there is a need for highly correlated climatic variables to build such model .

Multivariate Multisite Model MV.MS. Reg for water Demand Forecasting

Cheleng A.Arselan; J.Al-Kazwini; Muhannad; Rafa H.Shaker.Al-suhaili

Engineering and Technology Journal, 2010, Volume 28, Issue 13, Pages 2516-2529

A new multivariate multi site MV.MS.Reg model is developed in this
research depended on regression analysis mixed with Auto regressive multisite
Matalas model (AMMM)and used for water demand forecasting .This developed
model was applied to Kerkuk city as a case study for long term forecasting of
water demand for different types such as domestic demand,industrial,commercial
and public demand.This was done by dividing the city into four sites and
dividing the total water demand in each site into three types of
demand(domestic,industrial with commercial and public demand) .Each type of
water demand in each site was analyzed by multivariate regression base then the
cross correlation between this type of demand for the four sites were included in
the model using multi site Matalas model.Many explanatory variables were
concluded to be most effective factors affecting different types of demands such
as monthly temperature,monthly evaporation ,number of residential units
,number of industrial and commercial units and number of public units which
were forecasted successfully using Stochastic weather generation (SWG)