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Alam MM, Akter MY, Islam ARMT, Mallick J, Kabir Z, Chu R, Arabameri A, Pal SC, Masud MAA, Costache R, Senapathi V. A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models. J Environ Manage 2024; 351:119714. [PMID: 38056328 DOI: 10.1016/j.jenvman.2023.119714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 11/18/2023] [Accepted: 11/23/2023] [Indexed: 12/08/2023]
Abstract
Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.
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Affiliation(s)
- Md Mahfuz Alam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
| | - Mst Yeasmin Akter
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh.
| | - Abu Reza Md Towfiqul Islam
- Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh; Department of Development Studies, Daffodil International University, Dhaka, 1216, Bangladesh.
| | - Javed Mallick
- Department of Civil Engineering, King Khalid University, Abha, 62529, Saudi Arabia.
| | - Zobaidul Kabir
- University of Newcastle, School of Environmental and Life Sciences, Newcastle, 2258, Australia.
| | - Ronghao Chu
- China Meteorological Administration·Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou, 450003, China; Henan Institute of Meteorological Sciences, Henan Meteorological Bureau, Zhengzhou, 450003, China.
| | - Alireza Arabameri
- Department of Geomorphology, Tarbiat Modares University, Tehran, 14115-111, Iran.
| | - Subodh Chandra Pal
- Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.
| | - Md Abdullah Al Masud
- School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.
| | - Romulus Costache
- Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania; Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania; National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania; Research Institute of the University of Bucharest, 36-46 Bd. M. Kogalniceanu, 5th District, 050107, Bucharest, Romania.
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Ravindran SM, Bhaskaran SKM, Ambat SKN. A Deep Neural Network Architecture to Model Reference Evapotranspiration Using a Single Input Meteorological Parameter. Environ. Process. 2021; 8:1567-1599. [PMCID: PMC8486967 DOI: 10.1007/s40710-021-00543-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/22/2021] [Indexed: 06/02/2023]
Abstract
Hydro-agrological research considers the reference evapotranspiration (ETo), driven by meteorological variables, crucial for achieving precise irrigation in precision agriculture. ETo modelling based on a single meteorological parameter would be beneficial in places where the collection of climatic parameters is challenging. The aim of this research is to develop a deep neural network (DNN) architecture that predicts daily ETo with a single input parameter selected based on the feature importance (FI) score generated by the machine learning techniques, random forest (RF), and extreme gradient boosting (XGBoost). This study also investigated the potential of SHapley Additive exPlanations to interpret and validate the outcomes of the feature selection methods by assessing the contributions of each feature to the ETo prediction. These methods recommended solar radiation as a significant parameter in the datasets of three California Irrigation Management System (CIMIS) weather stations located in distinct ETo zones. Three ETo models (DNN-Ret, XGB-Ret, and RF-Ret) were built using solar radiation as the sole input, and CIMIS ETo as the output. The performance evaluation of the developed models proved that DNN-Ret outperformed XGB-Ret and RF-Ret regardless of the dataset, with coefficients of determination (R2) ranging from 0.914 to 0.954 in the local scenario, with an average decrease of 8–9.5% in mean absolute error and root mean squared error, and an improvement of 2.6–2.9% in Nash–Sutcliffe efficiency and 1.7–2% increase in R2. The overall result analysis highlighted the efficiency of DNN-Ret in the single input parameter based ETo modelling in diverse climatic zones.
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Affiliation(s)
- Sowmya Mangalath Ravindran
- Department of Computer Applications, Cochin University of Science and Technology, Kochi, Kerala 682022 India
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