A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models

被引：21
作者：
Alam, Md Mahfuz [1 ]
Akter, Mst. Yeasmin [1 ]
Islam, Abu Reza Md Towfiqul [1 ,2 ]
Mallick, Javed [3 ]
Kabir, Zobaidul [4 ]
Chu, Ronghao [5 ,6 ]
Arabameri, Alireza [7 ]
Pal, Subodh Chandra [8 ]
Masud, Md Abdullah Al [9 ]
Costache, Romulus [10 ,11 ,12 ,13 ]
Senapathi, Venkatramanan [14 ]
机构：
[1] Begum Rokeya Univ, Dept Disaster Management, Rangpur 5400, Bangladesh
[2] Daffodil Int Univ, Dept Dev Studies, Dhaka 1216, Bangladesh
[3] King Khalid Univ, Dept Civil Engn, Abha 62529, Saudi Arabia
[4] Univ Newcastle, Sch Environm & Life Sci, Newcastle, 2258, Australia
[5] China Meteorol Adm, Henan Key Lab Agrometeorol Support & Appl Tech, Zhengzhou 450003, Peoples R China
[6] Henan Meteorol Bur, Henan Inst Meteorol Sci, Zhengzhou 450003, Peoples R China
[7] Tarbiat Modares Univ, Dept Geomorphol, Tehran 14115111, Iran
[8] Univ Burdwan, Dept Geog, Bardhaman 713104, West Bengal, India
[9] Kyungpook Natl Univ, Sch Architecture Civil Environm & Energy Engn, Daegu 41566, South Korea
[10] Transilvania Univ Brasov, Dept Civil Engn, 5 Turnului Str, Brasov 500152, Romania
[11] Danube Delta Natl Inst Res & Dev, 165 Babadag St, Tulcea 820112, Romania
[12] Natl Inst Hydrol & Water Management, Bucuresti Ploiesti Rd 97 E,1st Dist, Bucharest 013686, Romania
[13] Univ Bucharest, Res Inst, 36-46 Bd M Kogalniceanu,5th Dist, Bucharest 050107, Romania
[14] Alagappa Univ Karaikudi, Dept Geol, Karaikkudi, Tamilnadu, India
来源：
JOURNAL OF ENVIRONMENTAL MANAGEMENT | 2024年 / 351卷
关键词：
Evapotranspiration prediction; Machine learning models; ANSIF; Climate variability; Bangladesh; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; PARTICLE SWARM OPTIMIZATION; LIMITED METEOROLOGICAL DATA; ESTIMATED CLIMATIC DATA; HUAI RIVER-BASIN; OF-THE-ART; PAN EVAPORATION; INTELLIGENCE MODELS; ALGORITHM;
D O I：
10.1016/j.jenvman.2023.119714
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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 R-2). 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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页数：20
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