Hybrid of Artificial Neural Network-Genetic Algorithm for Prediction of Reference Evapotranspiration (ET?) in Arid and Semiarid Regions

Abstract

Evapotranspiration is a principal requirement in designing any irrigation project, especially in arid and semiarid regions. Precise prediction of Evapotranspiration would reduce the squandering of huge quantities of water. Feedforward Backpropagation Neural Network (FFBPNN) model is employed in this study to evaluate the performance of Artificial Neural Networks (ANNs) in comparison with Empirical FAO Penman-Monteith (P-M) Equation in predicting reference evapotranspiration (ET₀); later, a hybrid model of ANN-Genetic Algorithm (GA) is proposed for the same evaluation function. Daily averages of maximum air temperature (T max ), minimum air temperature (T min ), relative humidity (R h ), radiation hours (R), and wind speed (U 2 ) from Mosul station (Nineveh, Iraq) are used as inputs to the ANN simulation model to predict ET₀ values obtained using P-M Equation. The main performance evaluation functions for both models are the Mean Square Errors (MSE) and the Correlation Coefficient (R 2 ). Both models yield promising results, but the hybrid model shows a higher efficiency in prediction of Evapotranspiration and could be recommended for modeling ET₀ in arid and semiarid regions.

Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis

“…The reduction in average values of lower and upper bounds (lower than the standard deviation (SD) of observed data) and the increase in observed data in 95PPU in uncertainty result in a more desirable uncertainty for each model. The uncertainty indices of d-factor and 95PPU for testing samples are presented in Table 2 for 64 models (four ANFIS-based models and 16 different input combinations, Equations (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)). In addition, the 95PPU values bracketed for Model 16 of each proposed model are given in Figure 5.…”

“…Research studies on hybrid techniques using data-driven models, such as artificial neural networks (ANNs), genetic programming (GP), and adaptive neuro-fuzzy inference systems (ANFISs), integrated with different optimization methods (e.g., particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE) algorithm) [9] have been published over the past decades and demonstrated positive outcomes for solving hydrology and water resource problems, such as rainfall runoff, river stage, evaporation, sediment, and groundwater, etc. [10][11][12][13][14][15]. Abrahart et al [16] used a pruning algorithm (PA) and a genetic algorithm (GA) to optimize data-driven models for runoff forecasting.…”

Temperature – Potential Evapotranspiration Relationship in Iraq Using Thornthwaite Method

“…The water balance equation for any natural area or water body indicates the relative values of inflow, outflow and change in water storage in the area or water body [3]. One of the most important outcomes in water balance equation for any natural area or water body is Evapotranspiration and it is also a crucial component of hydrologic cycle [4]. It can be defined as the combination of two separate processes through which, water is lost from the soil surface via evaporation process and from the crop by transpiration [5].…”

Data-Based Evapotranspiration Modeling

“…7.5. This chapter describes two different realms in evaporation modeling: [1] reference evapotranspiration (ET 0 ) with traditional models from the meteorological data and [2] evaporation modeling with the data-based modeling concepts. In Sect.…”

“…Recently, Kişi and Tombul [18] have used a fuzzy genetic (FG) approach for modeling monthly pan evaporation. Abdullah et al [1] used a hybrid of Artificial Neural Network-Genetic Algorithm for (ET 0 ) estimation in Iraq. El-Shafie et al [8] suggest that the ANN model is better than the ARMA model for multi-lead ahead prediction of evapotranspiration.…”