Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment

Abstract

Exact estimation of evaporation rates is very important in a proper planning and efficient operation of water resources projects and agricultural activities. Evaporation is affected by many driving forces characterized by nonlinearity, non-stationary, and stochasticity. Such factors clearly hinder setting up rigorous predictive models. This study evaluates the predictability of coupling the additive regression model (AR) with four ensemble machine-learning algorithms—random Subspace (RSS), M5 pruned (M5P), reduced error pruning tree (REPTree), and bagging for estimating pan evaporation rates. Meteorological data encompass maximum temperature, minimum temperature, mean temperature, relative humidity, and wind speed from three different agroclimatic stations in Iraq (i.e., Baghdad, Mosul, and Basrah) were utilized as predictor parameters. The regression model in addition to the sensitivity analysis was employed to identify the best-input combinations for the evaluated methods. It was demonstrated that the AR-M5P estimated the evaporation with higher accuracy than others when combining wind speed, relative humidity, and the minimum and mean temperatures as input parameters. The AR-M5P model provided the best performance indicators, i.e., MAE = 33.82, RMSE = 45.05, RAE = 24.75, RRSE = 28.50, and r = 0.972 for Baghdad; MAE = 25.82, RMSE = 35.95, RAE = 23.75, RRSE = 29.64, and r = 0.956 for Mosul station, respectively. The outcomes of this study proved the superior performance of the hybridized methods in addressing such intricate hydrological relationships and hence could be employed for other environmental problems.

Proposing Optimal Locations for Runoff Harvesting and Water Management Structures in the Hami Qeshan Watershed, Iraq

“…Further research must be undertaken, which accounts for surface runoff volume generated within HQW. More studies can also compare the findings of the applied methodology with those of other approaches, such as machine learning [29,123,124] and TOPSIS [23], to strengthen the accuracy of the implemented model. All previous recommendations aim to improve the reliability and predictive capability of the proposed methodology and establish a practical framework for developing a sustainable and comprehensive water resource management scheme.…”

Estimating the monthly pan evaporation with limited climatic data in dryland based on the extended long short-term memory model enhanced with meta-heuristic algorithms

“…In general, the direct measurements method (e.g., Class A pan, Lysimeter group) is largely restricted due to the limitation of experimental conditions in dryland 14 – 16 , and the physically-based methods (e.g., Dalton model, FAO-56 Penman–Monteith method, etc.) have the drawbacks that the estimated results are very sensitive to the errors of parameters 17 , 18 , and the key meteorological factors(e.g., relative humidity, latent heat of evaporation, radiation) are sometimes difficult to be measured in the arid sand land 19 , 20 . Therefore, it is necessary to construct the data-driven models to estimate the Ep with less meteorological information.…”

“…Accurate estimation of evaporation by using is a significant issue in ecological management [6][7][8][9][10][11] , especially in arid sand land, where the stability and sustainability of the artificially re-vegetated belts depend on the effective utilization of the limited available water resources 12,13 .In general, the direct measurements method (e.g., Class A pan, Lysimeter group) is largely restricted due to the limitation of experimental conditions in dryland [14][15][16] , and the physically-based methods (e.g., Dalton model, FAO-56 Penman-Monteith method, etc.) have the drawbacks that the estimated results are very sensitive to the errors of parameters 17,18 , and the key meteorological factors(e.g., relative humidity, latent heat of evaporation,…”

“…In general, the direct measurements method (e.g., Class A pan, Lysimeter group) is largely restricted due to the limitation of experimental conditions in dryland [14][15][16] , and the physically-based methods (e.g., Dalton model, FAO-56 Penman-Monteith method, etc.) have the drawbacks that the estimated results are very sensitive to the errors of parameters 17,18 , and the key meteorological factors(e.g., relative humidity, latent heat of evaporation,…”

Introducing an Innovative Design Approach for Drainage Systems: Facilitating Shallow Aquifer Recharge and Mitigating Flooding

“…Spatial and numerical modelling have been used to study groundwater in urban areas around the world [16][17][18][19][20][21][22][23][24][25]. In addition, machine learning hybrid models have also been considered in groundwater modeling and other water-related subjects owing to their superior performance in handling the complexity of water resources phenomena represented by non-linearity, non-stationarity, and stochasticity [22,[25][26][27]. A recharge model was proposed for an urban lake to identify the interaction between groundwater and vegetation restoration [28].…”