Premium
A STATISTICAL METHODOLOGY FOR PREDICTING THE POLLUTANTS IN A RIVER 1
Author(s) -
Nour ARazek A. Abouel
Publication year - 1972
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1972.tb05089.x
Subject(s) - pollutant , water quality , environmental science , streams , regression analysis , linear regression , pollution , hydrology (agriculture) , set (abstract data type) , computer science , environmental engineering , engineering , machine learning , computer network , ecology , chemistry , geotechnical engineering , organic chemistry , biology , programming language
. Urban and industrial areas continue to expand and consequently, to create serious water pollution problems to natural streams. The need for the development of accurate, reliable, and sensitive water quality prediction models is most desirable. The first objective of this research is to set guidelines for dividing a natural stream into more or less independent reaches based on some criteria. The second objective is to obtain the predicting equations of the water pollutants in a selected stream. The preliminary phase of this research evaluated water quality data sampled from the Pearl River which flows southwest and then turns south through the states of Mississippi and Louisiana. This evaluation served as guidelines to divide the total river basin into reaches (subsystems) appropriate to the objective of this research. Subsequent to this subsystem assignment, a stepwise multiple regression FORTRAN program was used to regress the pollutants (dependent variables) for both time and space on their water characteristics (independent variables). Based on the results obtained, the proposed statistical approach provides a practical tool for developing regression equations for the purpose of water pollutants' prediction.