Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

Abstract

Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor-predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash-Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2-55.4% compared to the other AI models.

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

“…As in Fig. 1, the ANFIS model in five layers consisting of two inputs and one output described in the following steps [36], [49]- [51]: Typically, any parameterized functions may be a membership function, i.e., for a linguistic label or a fuzzy set; generalized Bell, trapezoidal, Form of the triangle, or Gaussian. E.g., the Gaussian membership function described as follows by a couple of parameters (c, σ):…”

Environmental assessment of physical-chemical features of Lake Nasser, Egypt

“…The River Nile is the most important freshwater supply in Egypt for thousands of years; it provides renewable water supply such as drinking, irrigation, and canalization in the Nile Valley and the Delta Region (Goher et al 2014;Ghodeif et al 2016;Negm 2017). Lake Nasser was generated by the construction of the Aswan High Dam between January 1964and June 1968(Abd El-Monsef et al 2015El Gamal and Zaki 2017;Salih et al 2019). The area of the Lake is about 5000 km 2 (Farhat and Aly 2018).…”

Hydraulic-pressure-following control of an electronic hydraulic brake system based on a fuzzy proportional and integral controller

“…Oshima et al (2011) took the hydraulic pressure of the main cylinder directly as the control variable, being easy to observe and realize, but did not achieve accurate control in the low pressure range. Salih et al (2019) proposed a new method, called a "coperative active neuro-fuzzy inference system" for modeling evaporation using meteorological variables, which could only simulate evaporation from average temperature and relative humidity; but the efficiency of the Nash-Sutcliffe model reaches 0.93, which is much superior. Baghban et al (2019) gathered 1277 experimental data points of different nanofluid relative viscosities based on a review of the literature, and also established a fuzzy inference system based on an accommodative network to construct a common model.…”