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

Table 2

Regression model polynomial of the second degree.

Model	Depth	Regression equation
y = ax^2 + bx + c	0.13 ≤ x < 0.31	Temp (2) = 368.68x^2 − 154.22x + 35.918
		pH (2) = −797.13x^2 + 334.61x − 21.488
		ORP (2) = 68081x^2 − 27968x + 2434.7
		DO (2) = −991.91x^2 + 393.23x − 19.756
		RES (2) = 1654.4x^2 − 676.1x + 75.934
		SAL (2) = −7180x^2 + 2967.8x − 221.95
		SSG (2) = −5779.4x^2 + 2389.9x − 182.11
		TN (2) = 1955.9x^2 − 733.97x + 112.76
		cd (2) = −8.1618x^2 + 3.4154x − 0.2671
		cu (2) = −6.875x^2 + 2.8562x − 0.1761
		zn (2) = 1.0662x^2 − 0.629x + 0.1258
		pb (2) = 0.1838x^2 − 0.1085x + 0.023
		fe (2) = 33.603x^2 − 15.626x + 2.1925
	0.31 ≤ x < 1.34	Temp (2) = 714.29x^2 − 497.14x + 108.07
		pH (2) = −491.43x^2 + 334.51x − 47.783
		ORP (2) = 18814x^2 − 13666x + 2469
		DO (2) = −1695x^2 + 1132.8x − 177.86
		RES (2) = −14000x^2 + 9660x − 1633.2
		SAL (2) = 21790x^2 − 15052x + 2616.4
		SSG (2) = 16771x^2 − 11584x + 2011.3
		TN (2) = −22200x^2 + 15258x − 2547.6
		cd (2) = −3.4286x^2 + 2.3943x − 0.3757
		Cu (2) = −5.8571x^2 + 4.1986x − 0.6647
		zn (2) = −1.4286x^2 + 1.1143x − 0.1761
		pb (2) = 0.1429x^2 − 0.0414x + 0.0051
		fe (2) = −357x^2 + 252.43x − 43.387
	0.3 ≤ x < 0.38	Total pb (2) = 506.67x^2 − 322.77x + 51.42

(2) refers to the estimated elements in Lake Manzala by regression model.