Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment.
Journal:
Scientific reports
Published Date:
Jun 20, 2025
Abstract
Evaporation represents a fundamental hydrological cycle process that demands dependable methods to quantify its fluctuation to ascertain sustainable agriculture, irrigation systems, and overall water resource management. Meteorological variables such as relative humidity, temperature, wind speed, and sunshine hours affect evaporation non-linearly, resulting in challenges while developing prediction models. To combat this, the study aimed to develop robust models for estimating evaporation in semi-arid environments by applying machine learning techniques. Daily meteorological datasets (from January 2000 to December 2010) for the above variables (input) were collected from the Sidi Yakoub meteorological station in the Wadi Sly basin, Algeria. Conventional deep neural network (DNN) coupled with support vector machine (SVM), Bayesian additive regression trees (BART), random subspace (RSS), M5 pruned, and random forest (RF) were used for developing prediction models using various input variable combinations. Model performances were compared using mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R), Nash-Sutcliffe efficiency (NSE) coefficient, and percentage bias (PBIAS). Results indicated comparatively better performance for hybrid models (DNN-SVM, DNN-BART, DNN-RSS, DNN-M5 pruned, and DNN-RF) than conventional models (standalone DNN). Among hybrid models, the DNN-SVM model outperformed others with high accuracy and performance and fewer statistical errors in the daily pan evaporation prediction during the testing phase (R²=0.65, RMSE = 3.00 mm, MAE = 2.13, NSE = 0.65, and PBIAS = 3.54). DNN-RF was in the second rank for the prediction with R of 0.64, RMSE of 3.00 mm, MAE of 2.16, NSE of 0.64, and PBIAS = 0.41. While the standalone DNN model gave the lowest results with MAE of 4.87, RMSE of 5.00 mm, and NRMSE of 0.65. The present framework's success in Algeria's Wadi Sly basin highlights its potential for scalable adoption in irrigation scheduling and drought resilience strategies, yielding implementable steps for policymakers, addressing climate-driven water scarcity. Future research should explore integrating real-time climate projections and socio-hydrological variables to improve predictive adaptability across diverse agroecological zones.
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