Sökning: WFRF:(Raza N) >
Data intelligence a...
Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration
-
- Elbeltagi, Ahmed (författare)
- Faculty of Agriculture, Agricultural Engineering Department, Mansoura University, Mansoura, 35516, Egypt
-
- Raza, Ali (författare)
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China
-
- Hu, Yongguang (författare)
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, 212013, People’s Republic of China
-
visa fler...
-
- Al-Ansari, Nadhir, 1947- (författare)
- Luleå tekniska universitet,Geoteknologi
-
- Kushwaha, N. L. (författare)
- Division of Agricultural Engineering, ICAR–Indian Agriculture Research Institute, New Delhi, 110012, India
-
- Srivastava, Aman (författare)
- Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, 721302, West-Bengal, India
-
- Kumar Vishwakarma, Dinesh (författare)
- Department of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India
-
- Zubair, Muhammad (författare)
- School of Transportation, Southeast University, Nanjing, 21009, China
-
visa färre...
-
(creator_code:org_t)
- 2022-05-06
- 2022
- Engelska.
-
Ingår i: Applied water science. - : Springer. - 2190-5487 .- 2190-5495. ; 12:7
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://ltu.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (Tmax, Tmin), average relative humidity (RHavg), average wind speed (Ux), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that Tmin, RHAvg, Ux, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Geoteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Geotechnical Engineering (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Jordbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Agricultural Science (hsv//eng)
Nyckelord
- Reference evapotranspiration
- Additive regression
- Sensitivity and regression analysis
- Machine learning
- Hydrological modeling
- Soil Mechanics
- Geoteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas