On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: case studies of river and lake in USA
Abstract Chlorophyll-a is one of the main indicators for water quality (WQ) analysis in environmental monitoring of aquatic ecosystems. WQ degradation is mostly a result of the increase of the concentration of chlorophyll-a in a waterbody, however, proper estimation of daily chlorophyll-a concentration is a complex problem. In this study, the classic extreme learning machine (ELM), group method of data handling (GMDH), random forest (RF), classification and regression tree (CART), and a novel integrated Bat-ELM model (with the bat optimization algorithm) were developed and applied to predict daily chlorophyll-a (Chl-a) concentration in river and lake ecosystems. Input parameters including turbidity (TU), pH, specific conductance (SC), water temperature (TE), and periodicity were applied as the influential elements for estimating daily Chl-a concentration for two different USGS stations. General results based on RMSE values indicated that the Bat-ELM as the most robust model improved the performance of standard ELM, GMDH, RF, and CART models during the testing procedure by 20.7%, 23.9%, 18.3%, and 27.4% in USGS no. 05543010 and 13.8%, 16.8%, 17.5%, and 52.0% in USGS no. 09014050 in terms of the 7th input-combination, respectively. Moreover, the results revealed that periodicity is the most effective input parameter that considered as the last scenario (input combination) on daily chlorophyll-a (Chl-a) concentration.