Abstract
Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EPm estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), Willmott index (WI), and relative error (RE)) and graphical interpretation (time variation plot, scatter plot, relative error plot, and Taylor diagram) were performed for the modeling evaluation. The results of appraisal showed that the CANFIS-1 model with six input variables provided better NRMSE (0.1364, 0.0904, 0.0947, and 0.0898), NSE (0.9439, 0.9736, 0.9703, and 0.9799), PCC (0.9790, 0.9872, 0.9877, and 0.9922), and WI (0.9860, 0.9934, 0.9927, and 0.9949) values for Pantnagar station, and NRMSE (0.1543, 0.1719, 0.2067, and 0.1356), NSE (0.9150, 0.8962, 0.8382, and 0.9453), PCC (0.9643, 0.9649, 0.9473, and 0.9762), and WI (0.9794, 0.9761, 0.9632, and 0.9853) values for Nagina stations in all applied modeling scenarios for estimating the monthly EPm. This study also confirmed the supremacy of the proposed integrated GT-CANFIS model under four different scenarios in estimating monthly EPm. The results of the current application demonstrated a reliable modeling methodology for water resource management and sustainability.
Highlights
The evaporation process is a crucial parameter in the global hydrological cycle, and is defined as the transformation of water from the liquid phase to water vapor [1]
The first phase was established to extract the input variables relating to the monthly EPm using the Gamma test
The second phase of modeling was employed to estimate the value of monthly EPm using different artificial intelligent (AI) models, including co-active neuro-fuzzy inference system (CANFIS) and Multilayer Perceptron Neural Network (MLPNN) at Pantnagar station and Nagina station, India
Summary
The evaporation process is a crucial parameter in the global hydrological cycle, and is defined as the transformation of water from the liquid phase to water vapor [1]. Open water surface evaporation is measured by employing two methods: (i) direct measurement by pan evaporimeters, and (ii) indirect measurement using empirical and semi-empirical equations based on climatic variables [7]. The direct measurement of evaporation using pan evaporimeters is prone to several sources of error due to multiple factors, such as animal activity in and around the pan, debris in water, the construction material of the pan, the size of the pan, strong wind circulation, exposure to the pan, and the measurement of water depth in the pan [4,8,9]. The estimation of monthly pan evaporation (EPm ) using direct measurement can be a tedious, expensive, and time-consuming task [10]. The introduction of robust and reliable intelligent models is a hot topic in the field of hydrology [11]
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