Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation

Abstract Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.

[1]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[2]  Vijay P. Singh,et al.  Evaluation of gene expression programming approaches for estimating daily evaporation through spatial and temporal data scanning , 2014 .

[3]  Yu Feng,et al.  Evaluation of artificial intelligence models for actual crop evapotranspiration modeling in mulched and non-mulched maize croplands , 2018, Comput. Electron. Agric..

[4]  Ozgur Kisi,et al.  Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran , 2014 .

[5]  Yu Feng,et al.  Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation , 2019, Energy Conversion and Management.

[6]  Kittisak Jermsittiparsert,et al.  Neuro-fuzzy estimation of reference crop evapotranspiration by neuro fuzzy logic based on weather conditions , 2020, Comput. Electron. Agric..

[7]  Yu Feng,et al.  Impacts of climatic variables on reference evapotranspiration during growing season in Southwest China , 2019, Agricultural Water Management.

[8]  Ningbo Cui,et al.  Development of data-driven models for prediction of daily global horizontal irradiance in Northwest China , 2019, Journal of Cleaner Production.

[9]  Ningbo Cui,et al.  National-scale development and calibration of empirical models for predicting daily global solar radiation in China , 2020 .

[10]  Jalal Shiri,et al.  Comprehensive assessment of 12 soft computing approaches for modelling reference evapotranspiration in humid locations , 2019, Meteorological Applications.

[11]  Jalal Shiri,et al.  Data splitting strategies for improving data driven models for reference evapotranspiration estimation among similar stations , 2019, Comput. Electron. Agric..

[12]  Junliang Fan,et al.  Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China , 2021, Comput. Electron. Agric..

[13]  Slavisa Trajkovic,et al.  Comparative analysis of 31 reference evapotranspiration methods under humid conditions , 2011, Irrigation Science.

[14]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[15]  Min Wu,et al.  Uncertainty analysis of artificial intelligence modeling daily reference evapotranspiration in the northwest end of China , 2020, Comput. Electron. Agric..

[16]  A. A. Alazba,et al.  Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate , 2016 .

[17]  Ningbo Cui,et al.  Estimation of soil temperature from meteorological data using different machine learning models , 2019, Geoderma.

[18]  N. S. Raghuwanshi,et al.  Artificial neural networks approach in evapotranspiration modeling: a review , 2010, Irrigation Science.

[19]  Vassilis Z. Antonopoulos,et al.  Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables , 2017, Comput. Electron. Agric..

[20]  Amir Mosavi,et al.  Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation , 2021 .

[21]  Junliang Fan,et al.  A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation , 2020 .

[22]  Ningbo Cui,et al.  Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China , 2021, Comput. Electron. Agric..

[23]  Gorka Landeras,et al.  Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain) , 2008 .

[24]  Tienfuan Kerh,et al.  Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone , 2010 .

[25]  S. Mehdizadeh,et al.  Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm , 2020, Agricultural Water Management.

[26]  Wenzhao Liu,et al.  Spatiotemporal characteristics of reference evapotranspiration during 1961-2009 and its projected changes during 2011-2099 on the Loess Plateau of China , 2012 .

[27]  Guy Fipps,et al.  Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages , 2016 .

[28]  Lifeng Wu,et al.  Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data , 2019, Agricultural Water Management.

[29]  Haoru Li,et al.  Machine learning models to quantify and map daily global solar radiation and photovoltaic power , 2020 .

[30]  H. Cai,et al.  Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China , 2018, Agricultural and Forest Meteorology.

[31]  Yu Feng,et al.  Evaluation of seasonal evapotranspiration of winter wheat in humid region of East China using large-weighted lysimeter and three models , 2020 .

[32]  Matheus Mendes Reis,et al.  Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data , 2019, Comput. Electron. Agric..

[33]  O. Kisi,et al.  Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration , 2020, Environmental Science and Pollution Research.

[34]  Jalal Shiri,et al.  Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology , 2018, Journal of Hydrology.

[35]  Jalal Shiri,et al.  Modeling reference evapotranspiration in island environments: Assessing the practical implications , 2019, Journal of Hydrology.

[36]  Jalal Shiri,et al.  Supplanting missing climatic inputs in classical and random forest models for estimating reference evapotranspiration in humid coastal areas of Iran , 2020, Comput. Electron. Agric..

[37]  Yu Feng,et al.  Water use efficiency and its drivers in four typical agroecosystems based on flux tower measurements , 2020 .

[38]  Ningbo Cui,et al.  Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. , 2017 .

[39]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[40]  Yixuan Zhang,et al.  Comparison of BP, PSO-BP and statistical models for predicting daily global solar radiation in arid Northwest China , 2019, Comput. Electron. Agric..

[41]  Omid Bozorg-Haddad,et al.  Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm , 2020, Journal of Hydrology.

[42]  J. Shiri,et al.  Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions , 2020 .

[43]  Lifeng Wu,et al.  Estimation of daily dew point temperature by using bat algorithm optimization based extreme learning machine , 2020 .

[44]  Yu Feng,et al.  Hybrid particle swarm optimization with extreme learning machine for daily reference evapotranspiration prediction from limited climatic data , 2020, Comput. Electron. Agric..

[45]  Yu Feng,et al.  High-resolution assessment of solar radiation and energy potential in China , 2021, Energy Conversion and Management.

[46]  Lucas Borges Ferreira,et al.  Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach , 2019, Journal of Hydrology.

[47]  Ningbo Cui,et al.  Improvement of Makkink model for reference evapotranspiration estimation using temperature data in Northwest China , 2018, Journal of Hydrology.

[48]  Anurag Malik,et al.  Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches , 2019, Hydrological Sciences Journal.

[49]  Min Yan Chia,et al.  Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine , 2021 .

[50]  Jia Yue,et al.  Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China , 2017 .