Open AccessSurface water resource and effect of weather parameters in estimating the annual rainfall: A case study in Lebanon
Author(s) -
Youssef Kassem,
Hüseyin Gökçekuş,
Julia Aljamal
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/800/1/012028
Subject(s) - environmental science , latitude , altitude (triangle) , longitude , wind speed , mean squared error , relative humidity , water cycle , meteorology , water resources , geographic coordinate system , resource (disambiguation) , climatology , atmospheric sciences , statistics , mathematics , geography , computer science , geology , geometry , computer network , ecology , geodesy , biology
The quality and quantity of freshwater resources are continually decreasing in the world. The objective of this paper is to review the literature on the water resource with a focus on the surface water, quality of surface water in terms of physical and chemical properties in different locations in Lebanon. Moreover, one of the most important sources influencing the surface water is rainfall. Forecasting rainfall is one of the most essential issues in the hydrological cycle. It is very challenging because is still not possible to develop an ideal model given the uncertainty and unexpected variation. In the present study, prediction models using artificial neural networks (ANN) and multiple linear regressions (MLR) are developed to estimate the annual rainfall as a function of weather parameters and geographical coordinates. The annual data used in this study are recorded in 1942 locations in Lebanon. The latitude, longitude, and altitude of the location, global solar radiation, average temperature, wind speed, and relative humidity are used as the input variables and annual rainfall is estimated as the output variable. The measured values are compared versus those predicted by the ANN and MLR models by evaluating R-squared and Root mean squared error.