Estimation of parameters on Texas reservoirs using least absolute shrinkage and selection operator

Abstract

This paper is to analyze and estimate Texas reservoirs of monitored water supply reservoirs about 89.8% full until December 26, 2018 using LASSO methods and predict its water reservoirs. Data collects January 2017, April 2017, July 2017, October 2017, January 2018, April 2018, July 2018, October 2018 and December 26, 2018 for 118 lakes or dams from Texas, USA. Variables include percent full, water level, height above conservation pool, reservoir storage, conservation storage, conservation capacity, and surface area. The least absolute shrinkage and selection operator (LASSO) methods are stepwise selections, net and external cross validation, group LASSO, and adaptive LASSO. Elastic net selection find the minimum validation average square error, conservation storage; stepwise selection picks the minimum values of adjust R2 (adjust R2 statistic), Akaike’s information criterion, corrected Akaike’s information criterion, Bayesian information criterion, Cp statistic, Schwarz Bayesian information criterion in the model. The results reveal that height above conservation pool along with conservation capacity has very strong significant level for percent full among all of variables to be chosen in the model. The percent full at Texas reservoirs of monitored water supply reservoirs is affected directly by the height above conservation pool. It has a trend for the height above conservation pool with quadratic pattern in the future.