in this research prediction methods of artificial neural network and chaos theory are employed to predict daily, weekly and monthly runoff. for this, runoff series data observed at pole-kohneh located in the qareh-soo river. the nonlinear predictions of chaos are found to be in close agreement with the observed runoff, with high correlation coefficient for daily and weekly time scales. predicte...

This paper describes a software tool called GROMIT and associated methods being developed by the Statistical Sciences Group (D-1) at Los Alamos National Laboratory in support of determining dependency structures describing large system behavior. While focused on the problem of reliability prediction, the discussion is relevant to other analytic situations such as determining the actors and beha...

In both the design of parallel computer systems and the development of applications, it is very important to have good performance prediction tools. This paper describes a new approach -PetaSIM, which is designed for the rapid prototyping stage of machine or application design. Computers, networks and applications are described as objects in a Java IDL (Interface Definition Language) with speci...

Modern companies accumulate a vast amount of customer data that can be used for creating a personalized experience. Analyzing this data is difficult and most business intelligence tools cannot cope with the volume of the data. One example is churn prediction, where the cost of retaining existing customers is less than acquiring new ones. Several data mining and machine learning approaches can b...

Load prediction is an important tool for grid utilities and power companies for managing the power system. This has traditionally mainly been applied at transmission and subtransmission system levels. However, as the traditional grid evolves into a smart grid, load prediction at smaller scales becomes necessary for efficient management and operation. In this paper we investigate how the qualita...

a fundamental study of scale formation of calcium carbonate (caco3) for producing oil wells hasbeen carried out. this article presents the study of the prediction of salt deposition in two differentsynthetic formation waters and investigates the effects of temperature and pressure on calciumcarbonate precipitation. the dependence of the induction period of the precipitation of calciumcarbonate ...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...