Comparison of Evolving Connectionist Systems (ECoS) and Neural Networks for Modelling Daily Pan Evaporation from Algeria
Evaporation (EP) from dams’ reservoirs measured using pans is one of the most important methods adopted for quantifying the loss of water through evaporation. Black box artificial intelligence techniques (AI) have been developed as alternative approaches
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Contents 1 Introduction 2 Study Area and Data 3 Materials and Methods 3.1 Dynamic Evolving Neural-Fuzzy Inference Systems (DENFIS) 3.2 Artificial Neural Network (ANN) 3.3 Multiple Linear Regression (MLR) 3.4 Performance Indices 4 Results 4.1 Daily Pan Evaporation Estimation at Guelma Station
A. Sebbar Soil and Hydraulics Laboratory, Hydraulics Department, Faculty of Engineering Sciences, University Badji-Mokhtar Annaba, Annaba, Algeria e-mail: [email protected] S. Heddam (*) Hydraulics Division, Agronomy Department, Faculty of Science, Laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology, Skikda, Algeria e-mail: [email protected] O. Kisi School of Technology, Ilia State University, Tbilisi, Georgia e-mail: [email protected] L. Djemili Research Laboratory of Natural Resources and Adjusting, Hydraulics Department, Faculty of Engineering Sciences, University Badji-Mokhtar Annaba, Annaba, Algeria e-mail: [email protected] L. Houichi Department of Hydraulic, University of Batna 2, Batna, Algeria e-mail: [email protected] Abdelazim Negm, Abdelkader Bouderbala, Haroun Chenchouni, and Damia Barcelo (eds.), Water Resources in Algeria - Part I: Assessment of Surface and Groundwater Resources, Hdb Env Chem, DOI 10.1007/698_2020_527, © Springer Nature Switzerland AG 2020
A. Sebbar et al. 4.2 Daily Pan Evaporation Estimation at Jijel Station 5 Discussion 6 Conclusions 7 Recommendations References
Abstract Evaporation (EP) from dams’ reservoirs measured using pans is one of the most important methods adopted for quantifying the loss of water through evaporation. Black box artificial intelligence techniques (AI) have been developed as alternative approaches for quantifying evaporation, and several kinds of models have been proposed worldwide. The present study uses the measurement of several climatic variables such as air temperature, wind speed, and relative humidity to test the performances of new AI techniques called evolving connectionist systems (ECoS), applied for predicting daily evaporation from several dam reservoirs located in Algeria country. Two ECoS models, namely, (1) offline-based dynamic evolving neural-fuzzy inference systems named DENFIS_OF and (2) online-based dynamic evolving neural-fuzzy inference systems named DENFIS_ON, were applied and compared for predicting daily evaporation. The results using ECoS models were compared to multiple linear regression (MLR) and artificial neural network (ANN) models. From the results obtained, it is seen that the ECoS models could predict daily evaporation from dam reservoirs with better accuracy than the ANN and MLR models. Keywords ANN, Dam reservoirs, DENFIS, ECoS, Evaporation, MLR, Modelling
1 Introduction Exact quantification of the component of the water budget especially for surface water such as dam reservoirs is a challenge. It is necessary to have a clear knowledge of the water budget of a reservoir to meet to any possible demand for water and for a rational water planning and management. Among all the components of w
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