Modeling of type-II fuzzy logic system with uncertainty handling of groundwater level prediction

• Published in: Environmental earth sciences Vol. 81; no. 9
• Main Authors: Alsolai, Hadeel, Al-Wesabi, Fahd N., Hilal, Anwer Mustafa, Alamgeer, Mohammad, Al Duhayyim, Mesfer, Hamza, Manar Ahmed, Mahmood, Khalid, Rizwanullah, Mohammed
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-05-2022; Springer Nature B.V
• Subjects: Algorithms; Biogeosciences; Decomposition; Deep learning for earth resource and environmental remote sensing; Design; Earth and Environmental Science; Earth Sciences; Empirical analysis; Environmental Science and Engineering; Fuzzy logic; Geochemistry; Geology; Groundwater; Groundwater levels; Handling; Hydrology/Water Resources; Modelling; Optimization; Performance prediction; Predictions; Seabirds; Soft computing; Terrestrial Pollution; Thematic Issue; Uncertainty; Uncertainty analysis; Water resources; Water resources planning; Uncertainty handling; Groundwater level; Prediction model; Machine learning; Type II fuzzy logic; Ensemble models; Artificial intelligence
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-022-10379-9

Accurate multi-time scale prediction of groundwater level (GWL) is important for water resources planning and management. But it is difficult to achieve reliable and robust GWL predictive even by the use of soft-computing techniques which considers the uncontrollable error, indefinite input, and unneglectable uncertainty at the time of modeling process. Soft computing approaches integrated into the data pre-treatment approach, input selection model, or uncertainty analysis can be employed to resolve this issue. The design of extensive deterministic and uncertainty analysis of automated models for the prediction of GWL is yet to be extensively explored. In this aspect, this study focuses on the design of type-II fuzzy logic system with uncertainty handling (T2FLS-UH) for GWL prediction. The goal of the T2FLS-UH technique is for predicting the GWL with the consideration of uncertainty. The T2FLS-UH technique encompasses different stages of operations such as data preparation, feature selection, prediction, and membership function selection. Besides, an ensemble empirical mode decomposition (EEMD) technique is involved for the decomposition of the original signals as to various intrinsic mode functions (IMFs). In addition, the Boruta approach is used for the selection of appropriate input variables for the prediction process. Moreover, the T2FLS model is applied for the GWL prediction process to estimate the value for every IMF. To improvise the predictive performance, the membership function of the T2FLS technique is chosen optimally by seagull optimization (SGO) algorithm. A wide range of simulations was carried to highlight the enhanced predictive performance of the T2FLS-UH technique. The experimental values pointed out the supremacy of the T2FLS-UH technique over the recent state of art prediction techniques.