Komparasi Kajian Model Hidrologi Runtun Waktu Menggunakan Soft Computing (Studi Kasus : DAS Siak Hulu)

There are several methods that can be used to predict discharge some future time, but the results still have relatively high error value. The use of models softcomputing a method that can be used to model a hydrological analysis such as forecasting discharge. Adaptive Neuro Fuzzy Inference System (ANFIS) is one softcomputing models that have proven reliability in conducting hydrological analysis as to forecast the flood discharge, and long reservoir inflow of seawater intrusion.Reliability ANFIS models need to be tested in the analysis of hydrological especially to predict discharge.ANFIS models were built to predict discharge using the main data is data on Tapung river left in the year 2002-2012 (except on taun 2007). Then ANFIS foresees the discharge of each of the data sources and analyze the magnitude of the error forecasting results debit ANFIS models. Then the forecast results will be compared with the results of forecasting with ANN method.Results discharge by using ANFIS models showed excellent results with test parameter value statistical correlation coefficient (R) of more than 0.75 are included in the category of correlation is very strong. To forecast results debit ANFIS models, yielding a value of correlation (R) of 0.99 while the forecast results debit ANN method produces a value of correlation (R) of 0.94903

Model Prediksi Liku Kalibrasi Menggunakan Pendekatan Jaringan Saraf Tiruan (ZST) (Studi Kasus : Sub DAS Siak Hulu)

The purpose of this research is to predict the rating curve equation on the site Pantai Cermin Sub-watershed Siak Hulu Siak River in 2009. This is based due to the unavailability of rating curve equation on the site Pantai Cermin Sub-watershed Siak Hulu in 2009 until 2012 so hopefully this research can be used as an alternative method for determining the rating curve equation turns more quickly and accurately. This research was conducted using an artificial neural network approach to the backpropagation algorithm. Configuration of network model used is the single input and single output. This approach uses auxiliary program that is Matlab 7.7.0.471 (R2008b). Results of this research showed that the artificial neural network model results of training, testing and validation have a very good level of correlation with the value of R 0.92996, 0.94469 and 0.97513. The rating curve equation for 2009 is Q = 22.93 x H1.265 with the level of correlation R = 0.9975

Kajian Potensi Ketersedian Air Menggunakan Model Neraca Air Bulanan Thornthwaite-Mather (Studi Kasus : Sub DAS Subayang Kampar Kiri Hulu

Water quantity is an important factor in evaluating water resources. It must be measured directly, However not all of the watersheds has hydrology station, the quantity or the amount of the water can be measured by modeling. One of the simple models in estimating the potential of water each month is Thornthwaite-mather method. This method is based on water balance. The rain as an interior, the evapotranspiration and the discharge of water as superficial. The characteristics of the soil physically and the occlusionof land area as a processor. The aim of this research is to getan information about the appraising of the availability of the water per month. The result of this research shows that the appraising of the potential of the availability of water assumed that 50% of the surplus of the water will be become runoff. The Runoff computation of the surface will be start as soon as the dry season ending. (S>0)in which at this point, assumption 50% is used from the surplus that will be added to the run-off for the upcoming month in and the Runoffis the amount of the availability of the water in the sub watersheds Subayang of the water in the sub watersheds Subayang.In order to get the good result of the standard comparison.The result of the water measurement is better to be checked again