Forecasting Methods in Various Applications Using Algorithm of Estimation Regression Models and Converting Data Sets into Markov Model

Abstract

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various depths, and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model” (AERM). On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy (MGSE) in Queensland, Australia, is called, Converting Data Set into Markov Model (CDMM). It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error.

Prediction Using Markov Model and Hidden Markov Model for the States of Banknotes

“…This approach was applied in the context of a study focusing on water quality in the northern part of Lake Manzala over the period from January to March 2016. To assess and forecast the condition of the water in Lake Manzala, a sophisticated model encompassing both linear and non-linear regression analyses was developed, specifically tailored to the water elements present in the lake [4].…”