Evaluation of soft computing and regression-based techniques for the estimation of evaporation

Abstract

The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.

Towards a Comprehensive Assessment of Statistical versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

“…This assessment is based on pertinent studies in the literature. For instance, in a comparative study between the capability of machine learning versus ANN, and statistical technique versus MLR, for EP prediction, it was found that the model efficiency and correlation coefficient of the ANN was higher than the MLR model for the calibration and validation phases [20]. The same result has been noted for the superiority of ANFIS model over the MLR statistical model [74].…”

“…Similarly, data-driven approaches, including computational intelligence and machine learning are also suitable for estimating the EP. Recently, regular hybrid and integrative data-driven models (e.g., artificial neural networks (ANN) as well as support vector machine (SVM) and adaptive neuro-fuzzy inference systems (ANFIS)) have been used for estimating the EP [7,[12][13][14][15][16][17][18][19][20].…”

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

“…The results of this study were compared with the previous studies conducted on modeling pan-evaporation using several artificial intelligence (AI) techniques optimized by bio-inspired algorithms (Kumar et al, 2021;Majhi et al, 2020;Qasem et al, 2019;Salih et al, 2019;Singh et al, 2021). The previous studies reported the effective utility of hybrid AI methods for pan-evaporation at different locations in varying climates through statistical metrics and visual investigation.…”

Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete

“…m n are the regression coefficients, and C is constant. MLR models were the usual method for estimating responses between a dependent variable and various independent factors where the dependent variable and independent variables had a linear connection [35].…”