Content area
Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.
Details
Wind speed;
Air temperature;
Water use;
Evapotranspiration;
Relative humidity;
Gene expression;
Water resources management;
Climatic conditions;
Training;
Climatic data;
Water management;
Hydrologic cycle;
Water resources;
Resource allocation;
Humidity;
Hydrology;
Irrigation water;
Urban areas;
Irrigation systems;
Allocations;
Aridity
; Vishwakarma, Dinesh Kumar 2
; Acharki, Siham 3
; Al-Ansari, Nadhir 4 ; Alshehri, Fahad 5 ; Elbeltagi, Ahmed 6 1 Jiangsu University, School of Agricultural Engineering, Zhenjiang, People’s Republic of China (GRID:grid.440785.a) (ISNI:0000 0001 0743 511X); King Saud University, Department of Geology and Geophysics, College of Science, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396)
2 G.B. Pant University of Agriculture and Technology, Department of Irrigation and Drainage Engineering, Pantnagar, India (GRID:grid.440691.e) (ISNI:0000 0001 0708 4444)
3 Abdelmalek Essaadi University, Department of Earth Sciences, Faculty of Sciences and Technologies of Tangier (FSTT), Tétouan, Morocco (GRID:grid.251700.1) (ISNI:0000 0001 0675 7133)
4 Lulea University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Luleå, Sweden (GRID:grid.6926.b) (ISNI:0000 0001 1014 8699)
5 King Saud University, Department of Geology and Geophysics, College of Science, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396)
6 Mansoura University, Agricultural Engineering Department, Faculty of Agriculture, Mansoura, Egypt (GRID:grid.10251.37) (ISNI:0000 0001 0342 6662)