Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-05-06

AUTHORS

Ahmed Elbeltagi, Ali Raza, Yongguang Hu, Nadhir Al-Ansari, N. L. Kushwaha, Aman Srivastava, Dinesh Kumar Vishwakarma, Muhammad Zubair

ABSTRACT

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. More... »

PAGES

152

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13201-022-01667-7

DOI

http://dx.doi.org/10.1007/s13201-022-01667-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1147680531


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Faculty of Agriculture, Agricultural Engineering Department, Mansoura University, 35516, Mansoura, Egypt", 
          "id": "http://www.grid.ac/institutes/grid.10251.37", 
          "name": [
            "Faculty of Agriculture, Agricultural Engineering Department, Mansoura University, 35516, Mansoura, Egypt"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Elbeltagi", 
        "givenName": "Ahmed", 
        "id": "sg:person.016135255445.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016135255445.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.440785.a", 
          "name": [
            "School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Raza", 
        "givenName": "Ali", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.440785.a", 
          "name": [
            "School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hu", 
        "givenName": "Yongguang", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden", 
          "id": "http://www.grid.ac/institutes/grid.6926.b", 
          "name": [
            "Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Al-Ansari", 
        "givenName": "Nadhir", 
        "id": "sg:person.016404270621.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016404270621.45"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Division of Agricultural Engineering, ICAR\u2013Indian Agriculture Research Institute, 110012, New Delhi, India", 
          "id": "http://www.grid.ac/institutes/grid.418105.9", 
          "name": [
            "Division of Agricultural Engineering, ICAR\u2013Indian Agriculture Research Institute, 110012, New Delhi, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kushwaha", 
        "givenName": "N. L.", 
        "id": "sg:person.013725503705.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013725503705.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, 721302, Kharagpur, West-Bengal, India", 
          "id": "http://www.grid.ac/institutes/grid.429017.9", 
          "name": [
            "Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, 721302, Kharagpur, West-Bengal, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Srivastava", 
        "givenName": "Aman", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, 263145, Pantnagar, Uttarakhand, India", 
          "id": "http://www.grid.ac/institutes/grid.440691.e", 
          "name": [
            "Department of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, 263145, Pantnagar, Uttarakhand, India"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kumar Vishwakarma", 
        "givenName": "Dinesh", 
        "id": "sg:person.012152214511.76", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152214511.76"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Transportation, Southeast University, 21009, Nanjing, China", 
          "id": "http://www.grid.ac/institutes/grid.263826.b", 
          "name": [
            "School of Transportation, Southeast University, 21009, Nanjing, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zubair", 
        "givenName": "Muhammad", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00704-021-03863-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1142908771", 
          "https://doi.org/10.1007/s00704-021-03863-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature11295", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023565341", 
          "https://doi.org/10.1038/nature11295"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11442-013-1015-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034286989", 
          "https://doi.org/10.1007/s11442-013-1015-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00271-012-0336-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002499490", 
          "https://doi.org/10.1007/s00271-012-0336-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00704-009-0204-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047846391", 
          "https://doi.org/10.1007/s00704-009-0204-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11119-018-9607-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107055283", 
          "https://doi.org/10.1007/s11119-018-9607-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00271-012-0332-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035222029", 
          "https://doi.org/10.1007/s00271-012-0332-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00058655", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002929950", 
          "https://doi.org/10.1007/bf00058655"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s100440200011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025322802", 
          "https://doi.org/10.1007/s100440200011"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00521-020-04800-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1125330831", 
          "https://doi.org/10.1007/s00521-020-04800-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-40495-5_42", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014802672", 
          "https://doi.org/10.1007/978-3-642-40495-5_42"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-05-06", 
    "datePublishedReg": "2022-05-06", 
    "description": "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.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s13201-022-01667-7", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1051421", 
        "issn": [
          "2190-5487", 
          "2190-5495"
        ], 
        "name": "Applied Water Science", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "12"
      }
    ], 
    "keywords": [
      "data intelligence", 
      "additive regression", 
      "ET0 modeling", 
      "input parameters", 
      "big challenge", 
      "high accuracy", 
      "algorithm", 
      "model development", 
      "influential input parameters", 
      "intelligence", 
      "machine", 
      "dataset", 
      "UX", 
      "observed points", 
      "regression method", 
      "methodological framework", 
      "daily reference evapotranspiration", 
      "stations", 
      "model", 
      "framework", 
      "accuracy", 
      "data", 
      "subspace", 
      "input", 
      "climatic datasets", 
      "challenges", 
      "precision", 
      "modeling", 
      "speed", 
      "greater precision", 
      "critical importance", 
      "estimation", 
      "reference evapotranspiration", 
      "results", 
      "method", 
      "parameters", 
      "meteorological inputs", 
      "point", 
      "sensitivity analysis", 
      "purpose", 
      "analysis", 
      "development", 
      "ET0 values", 
      "scarcity", 
      "climatic data", 
      "meteorological stations", 
      "regression", 
      "ET0", 
      "importance", 
      "wind speed", 
      "studied stations", 
      "sunshine hours", 
      "values", 
      "climatic parameters", 
      "study", 
      "average wind speed", 
      "index", 
      "region", 
      "humidity", 
      "input climatic parameters", 
      "countries", 
      "hours", 
      "Pakistan", 
      "Tmin", 
      "regression analysis", 
      "evapotranspiration", 
      "minimum temperature", 
      "relative humidity", 
      "average relative humidity", 
      "semi-arid regions", 
      "temperature"
    ], 
    "name": "Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration", 
    "pagination": "152", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1147680531"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s13201-022-01667-7"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s13201-022-01667-7", 
      "https://app.dimensions.ai/details/publication/pub.1147680531"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:26", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_940.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s13201-022-01667-7"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s13201-022-01667-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s13201-022-01667-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13201-022-01667-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13201-022-01667-7'


 

This table displays all metadata directly associated to this object as RDF triples.

236 TRIPLES      22 PREDICATES      107 URIs      88 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s13201-022-01667-7 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N2e2303f01c2342c2aeba0a4d2fdce5d7
4 schema:citation sg:pub.10.1007/978-3-642-40495-5_42
5 sg:pub.10.1007/bf00058655
6 sg:pub.10.1007/s00271-012-0332-6
7 sg:pub.10.1007/s00271-012-0336-2
8 sg:pub.10.1007/s00521-020-04800-2
9 sg:pub.10.1007/s00704-009-0204-z
10 sg:pub.10.1007/s00704-021-03863-y
11 sg:pub.10.1007/s100440200011
12 sg:pub.10.1007/s11119-018-9607-0
13 sg:pub.10.1007/s11442-013-1015-9
14 sg:pub.10.1038/nature11295
15 schema:datePublished 2022-05-06
16 schema:datePublishedReg 2022-05-06
17 schema:description 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.
18 schema:genre article
19 schema:inLanguage en
20 schema:isAccessibleForFree true
21 schema:isPartOf N5fc3826e0d8d493b9c646cb96139259a
22 N870550a5e27f4464b99d90148e7244a1
23 sg:journal.1051421
24 schema:keywords ET0
25 ET0 modeling
26 ET0 values
27 Pakistan
28 Tmin
29 UX
30 accuracy
31 additive regression
32 algorithm
33 analysis
34 average relative humidity
35 average wind speed
36 big challenge
37 challenges
38 climatic data
39 climatic datasets
40 climatic parameters
41 countries
42 critical importance
43 daily reference evapotranspiration
44 data
45 data intelligence
46 dataset
47 development
48 estimation
49 evapotranspiration
50 framework
51 greater precision
52 high accuracy
53 hours
54 humidity
55 importance
56 index
57 influential input parameters
58 input
59 input climatic parameters
60 input parameters
61 intelligence
62 machine
63 meteorological inputs
64 meteorological stations
65 method
66 methodological framework
67 minimum temperature
68 model
69 model development
70 modeling
71 observed points
72 parameters
73 point
74 precision
75 purpose
76 reference evapotranspiration
77 region
78 regression
79 regression analysis
80 regression method
81 relative humidity
82 results
83 scarcity
84 semi-arid regions
85 sensitivity analysis
86 speed
87 stations
88 studied stations
89 study
90 subspace
91 sunshine hours
92 temperature
93 values
94 wind speed
95 schema:name Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration
96 schema:pagination 152
97 schema:productId N2e7982ede4504b1ba8da7c593daa3aa1
98 N3db2372181ea460dbd8b28931d876d8e
99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147680531
100 https://doi.org/10.1007/s13201-022-01667-7
101 schema:sdDatePublished 2022-06-01T22:26
102 schema:sdLicense https://scigraph.springernature.com/explorer/license/
103 schema:sdPublisher N2b11839d4d9e4a4d8bf99cc92aeaab0e
104 schema:url https://doi.org/10.1007/s13201-022-01667-7
105 sgo:license sg:explorer/license/
106 sgo:sdDataset articles
107 rdf:type schema:ScholarlyArticle
108 N0dabbd9e00b94e79890386b89f24fd37 rdf:first Ndd40ce4c236d465783011cff27c68670
109 rdf:rest Nc6d9029e46c94f6b9f4081dc12af55bd
110 N2b11839d4d9e4a4d8bf99cc92aeaab0e schema:name Springer Nature - SN SciGraph project
111 rdf:type schema:Organization
112 N2e2303f01c2342c2aeba0a4d2fdce5d7 rdf:first sg:person.016135255445.80
113 rdf:rest Nac0f31afa6fa4be795110d4aef6f9df2
114 N2e7982ede4504b1ba8da7c593daa3aa1 schema:name dimensions_id
115 schema:value pub.1147680531
116 rdf:type schema:PropertyValue
117 N395f0fe052944fe3815c078be369bb91 rdf:first sg:person.013725503705.94
118 rdf:rest N0dabbd9e00b94e79890386b89f24fd37
119 N3db2372181ea460dbd8b28931d876d8e schema:name doi
120 schema:value 10.1007/s13201-022-01667-7
121 rdf:type schema:PropertyValue
122 N5f313496d2684d4a9954512d5bd6c2d9 rdf:first N62698e9356fe4a608a70373393c38e0b
123 rdf:rest rdf:nil
124 N5fc3826e0d8d493b9c646cb96139259a schema:volumeNumber 12
125 rdf:type schema:PublicationVolume
126 N62698e9356fe4a608a70373393c38e0b schema:affiliation grid-institutes:grid.263826.b
127 schema:familyName Zubair
128 schema:givenName Muhammad
129 rdf:type schema:Person
130 N653ad11d6e2c47d798eece0f371e6b02 rdf:first Na542b1f3e9384cf68870cd59b9c0e1a7
131 rdf:rest N9888a4835ab44865a7a0c1a78973aa62
132 N870550a5e27f4464b99d90148e7244a1 schema:issueNumber 7
133 rdf:type schema:PublicationIssue
134 N9888a4835ab44865a7a0c1a78973aa62 rdf:first sg:person.016404270621.45
135 rdf:rest N395f0fe052944fe3815c078be369bb91
136 N9e5376cd71c840edb54fd60ebc7ce873 schema:affiliation grid-institutes:grid.440785.a
137 schema:familyName Raza
138 schema:givenName Ali
139 rdf:type schema:Person
140 Na542b1f3e9384cf68870cd59b9c0e1a7 schema:affiliation grid-institutes:grid.440785.a
141 schema:familyName Hu
142 schema:givenName Yongguang
143 rdf:type schema:Person
144 Nac0f31afa6fa4be795110d4aef6f9df2 rdf:first N9e5376cd71c840edb54fd60ebc7ce873
145 rdf:rest N653ad11d6e2c47d798eece0f371e6b02
146 Nc6d9029e46c94f6b9f4081dc12af55bd rdf:first sg:person.012152214511.76
147 rdf:rest N5f313496d2684d4a9954512d5bd6c2d9
148 Ndd40ce4c236d465783011cff27c68670 schema:affiliation grid-institutes:grid.429017.9
149 schema:familyName Srivastava
150 schema:givenName Aman
151 rdf:type schema:Person
152 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
153 schema:name Information and Computing Sciences
154 rdf:type schema:DefinedTerm
155 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
156 schema:name Artificial Intelligence and Image Processing
157 rdf:type schema:DefinedTerm
158 sg:journal.1051421 schema:issn 2190-5487
159 2190-5495
160 schema:name Applied Water Science
161 schema:publisher Springer Nature
162 rdf:type schema:Periodical
163 sg:person.012152214511.76 schema:affiliation grid-institutes:grid.440691.e
164 schema:familyName Kumar Vishwakarma
165 schema:givenName Dinesh
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152214511.76
167 rdf:type schema:Person
168 sg:person.013725503705.94 schema:affiliation grid-institutes:grid.418105.9
169 schema:familyName Kushwaha
170 schema:givenName N. L.
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013725503705.94
172 rdf:type schema:Person
173 sg:person.016135255445.80 schema:affiliation grid-institutes:grid.10251.37
174 schema:familyName Elbeltagi
175 schema:givenName Ahmed
176 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016135255445.80
177 rdf:type schema:Person
178 sg:person.016404270621.45 schema:affiliation grid-institutes:grid.6926.b
179 schema:familyName Al-Ansari
180 schema:givenName Nadhir
181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016404270621.45
182 rdf:type schema:Person
183 sg:pub.10.1007/978-3-642-40495-5_42 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014802672
184 https://doi.org/10.1007/978-3-642-40495-5_42
185 rdf:type schema:CreativeWork
186 sg:pub.10.1007/bf00058655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
187 https://doi.org/10.1007/bf00058655
188 rdf:type schema:CreativeWork
189 sg:pub.10.1007/s00271-012-0332-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035222029
190 https://doi.org/10.1007/s00271-012-0332-6
191 rdf:type schema:CreativeWork
192 sg:pub.10.1007/s00271-012-0336-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002499490
193 https://doi.org/10.1007/s00271-012-0336-2
194 rdf:type schema:CreativeWork
195 sg:pub.10.1007/s00521-020-04800-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1125330831
196 https://doi.org/10.1007/s00521-020-04800-2
197 rdf:type schema:CreativeWork
198 sg:pub.10.1007/s00704-009-0204-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1047846391
199 https://doi.org/10.1007/s00704-009-0204-z
200 rdf:type schema:CreativeWork
201 sg:pub.10.1007/s00704-021-03863-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1142908771
202 https://doi.org/10.1007/s00704-021-03863-y
203 rdf:type schema:CreativeWork
204 sg:pub.10.1007/s100440200011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025322802
205 https://doi.org/10.1007/s100440200011
206 rdf:type schema:CreativeWork
207 sg:pub.10.1007/s11119-018-9607-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107055283
208 https://doi.org/10.1007/s11119-018-9607-0
209 rdf:type schema:CreativeWork
210 sg:pub.10.1007/s11442-013-1015-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034286989
211 https://doi.org/10.1007/s11442-013-1015-9
212 rdf:type schema:CreativeWork
213 sg:pub.10.1038/nature11295 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023565341
214 https://doi.org/10.1038/nature11295
215 rdf:type schema:CreativeWork
216 grid-institutes:grid.10251.37 schema:alternateName Faculty of Agriculture, Agricultural Engineering Department, Mansoura University, 35516, Mansoura, Egypt
217 schema:name Faculty of Agriculture, Agricultural Engineering Department, Mansoura University, 35516, Mansoura, Egypt
218 rdf:type schema:Organization
219 grid-institutes:grid.263826.b schema:alternateName School of Transportation, Southeast University, 21009, Nanjing, China
220 schema:name School of Transportation, Southeast University, 21009, Nanjing, China
221 rdf:type schema:Organization
222 grid-institutes:grid.418105.9 schema:alternateName Division of Agricultural Engineering, ICAR–Indian Agriculture Research Institute, 110012, New Delhi, India
223 schema:name Division of Agricultural Engineering, ICAR–Indian Agriculture Research Institute, 110012, New Delhi, India
224 rdf:type schema:Organization
225 grid-institutes:grid.429017.9 schema:alternateName Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, 721302, Kharagpur, West-Bengal, India
226 schema:name Department of Civil Engineering, Indian Institute of Technology (IIT) Kharagpur, 721302, Kharagpur, West-Bengal, India
227 rdf:type schema:Organization
228 grid-institutes:grid.440691.e schema:alternateName Department of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, 263145, Pantnagar, Uttarakhand, India
229 schema:name Department of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, 263145, Pantnagar, Uttarakhand, India
230 rdf:type schema:Organization
231 grid-institutes:grid.440785.a schema:alternateName School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People’s Republic of China
232 schema:name School of Agricultural Engineering, Jiangsu University, 212013, Zhenjiang, People’s Republic of China
233 rdf:type schema:Organization
234 grid-institutes:grid.6926.b schema:alternateName Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
235 schema:name Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden
236 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...