变更

              
    

          
          
        
        

            
f
1
{
f
1
{
            
2
  "agreement": "true",
2
  "agreement": "true",
            
3
  "alternative_identifier": "10.2166/hydro.2024.268",
3
  "alternative_identifier": "10.2166/hydro.2024.268",
            
4
  "author": null,
4
  "author": null,
            
5
  "author_email": null,
5
  "author_email": null,
            
6
  "authors": [
6
  "authors": [
            
7
    {
7
    {
            
8
      "author_department": "Department of Plant and Soil Sciences",
8
      "author_department": "Department of Plant and Soil Sciences",
            
9
      "author_email": "ceschutte34@gmail.com",
9
      "author_email": "ceschutte34@gmail.com",
            
10
      "author_name": "Christiaan ",
10
      "author_name": "Christiaan ",
            
11
      "author_organization": "University of Pretoria",
11
      "author_organization": "University of Pretoria",
            
12
      "author_surname": "Schutte",
12
      "author_surname": "Schutte",
            
13
      "contact_same_as_author": "True"
13
      "contact_same_as_author": "True"
            
14
    },
14
    },
            
15
    {
15
    {
            
16
      "author_department": "Department of Plant and Soil Sciences",
16
      "author_department": "Department of Plant and Soil Sciences",
            
17
      "author_email": "vanderLaanM@arc.agric.za",
17
      "author_email": "vanderLaanM@arc.agric.za",
            
18
      "author_name": "Michael",
18
      "author_name": "Michael",
            
19
      "author_organization": "University of Pretoria",
19
      "author_organization": "University of Pretoria",
            
20
      "author_surname": "van der Laan"
20
      "author_surname": "van der Laan"
            
21
    },
21
    },
            
22
    {
22
    {
            
23
      "author_department": "Department of Geography, Geoinformatics 
23
      "author_department": "Department of Geography, Geoinformatics 
            
24
and Meteorology",
24
and Meteorology",
            
25
      "author_email": "barend.vandermerwe@up.ac.za",
25
      "author_email": "barend.vandermerwe@up.ac.za",
            
26
      "author_name": "Barend",
26
      "author_name": "Barend",
            
27
      "author_organization": "University of Pretoria",
27
      "author_organization": "University of Pretoria",
            
28
      "author_surname": "van der Merwe"
28
      "author_surname": "van der Merwe"
            
29
    }
29
    }
            
30
  ],
30
  ],
            
31
  "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B 
31
  "citation_title": "SCHUTTE C, VAN DER LAAN M and VAN DER MERWE B 
            
32
(2024) Leveraging historic streamflow and weather data with deep 
32
(2024) Leveraging historic streamflow and weather data with deep 
            
33
learning for enhanced streamflow predictions. Journal of 
33
learning for enhanced streamflow predictions. Journal of 
            
34
Hydroinformatics 26 (4) 835. http://doi.org/10.2166/hydro.2024.268.",
34
Hydroinformatics 26 (4) 835. http://doi.org/10.2166/hydro.2024.268.",
            
35
  "contact_person": [
35
  "contact_person": [
            
36
    {
36
    {
            
37
      "contact_department": "Department of Plant and Soil Sciences",
37
      "contact_department": "Department of Plant and Soil Sciences",
            
38
      "contact_email": "ceschutte34@gmail.com",
38
      "contact_email": "ceschutte34@gmail.com",
            
39
      "contact_name": "Christiaan ",
39
      "contact_name": "Christiaan ",
            
40
      "contact_orgnization": "University of Pretoria"
40
      "contact_orgnization": "University of Pretoria"
            
41
    }
41
    }
            
42
  ],
42
  ],
            
43
  "creator_user_id": "fa7af5b8-32a7-4df0-90a7-c49fbefb1fbd",
43
  "creator_user_id": "fa7af5b8-32a7-4df0-90a7-c49fbefb1fbd",
            
44
  "data_classification": "time series",
44
  "data_classification": "time series",
            
45
  "data_collection_organization": "Water Research Observatory",
45
  "data_collection_organization": "Water Research Observatory",
            
46
  "data_reference_date": [
46
  "data_reference_date": [
            
47
    {
47
    {
            
48
      "data_reference_date_from": "1979-10-01",
48
      "data_reference_date_from": "1979-10-01",
            
49
      "data_reference_date_to": "2002-02-22"
49
      "data_reference_date_to": "2002-02-22"
            
50
    }
50
    }
            
51
  ],
51
  ],
            
52
  "data_structure_category": "structured",
52
  "data_structure_category": "structured",
            
53
  "dataset_language": "English",
53
  "dataset_language": "English",
            
54
  "dataset_license_url": " 
54
  "dataset_license_url": " 
            
55
https://creativecommons.org/licenses/by-sa/4.0/",
55
https://creativecommons.org/licenses/by-sa/4.0/",
            
56
  "did_author_or_contact_organization_collect_the_data": "false",
56
  "did_author_or_contact_organization_collect_the_data": "false",
            
57
  "email": "vanderLaanM@arc.agric.za",
57
  "email": "vanderLaanM@arc.agric.za",
            
58
  "groups": [],
58
  "groups": [],
            
59
  "id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
59
  "id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
            
60
  "isopen": false,
60
  "isopen": false,
            
61
  "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow 
61
  "keywords": "GRU, LSTM, rainfall-runoff modelling, streamflow 
            
62
prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, 
62
prediction, deep learning, Steelpoort River, Olifants basin, CHIRPS, 
            
63
NASA POWER, ARC",
63
NASA POWER, ARC",
            
64
  "license": "Open (Creative Commons)",
64
  "license": "Open (Creative Commons)",
            
65
  "license_id": null,
65
  "license_id": null,
            
66
  "license_title": null,
66
  "license_title": null,
            
67
  "maintainer": null,
67
  "maintainer": null,
            
68
  "maintainer_email": null,
68
  "maintainer_email": null,
            
69
  "metadata_created": "2025-10-22T11:55:59.470668",
69
  "metadata_created": "2025-10-22T11:55:59.470668",
            
n
70
  "metadata_modified": "2026-04-08T14:42:55.694094",
n
70
  "metadata_modified": "2026-04-09T03:36:22.236258",
            
71
  "minimum_maximum_extent": [
71
  "minimum_maximum_extent": [
            
72
    {
72
    {
            
73
      "maximum_vertical_extent": "2263",
73
      "maximum_vertical_extent": "2263",
            
74
      "minimum_vertical_extent": "1336"
74
      "minimum_vertical_extent": "1336"
            
75
    }
75
    }
            
76
  ],
76
  ],
            
77
  "name": "deep-learning-for-streamflow-prediction-project-data",
77
  "name": "deep-learning-for-streamflow-prediction-project-data",
            
78
  "notes": "This study evaluates deep learning methods for daily 
78
  "notes": "This study evaluates deep learning methods for daily 
            
79
streamflow prediction in data-scarce contexts, using two headwater 
79
streamflow prediction in data-scarce contexts, using two headwater 
            
80
catchments of the Steelpoort River (Olifants River basin, South 
80
catchments of the Steelpoort River (Olifants River basin, South 
            
81
Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
81
Africa) as case studies. Gated Recurrent Unit (GRU) and Long 
            
82
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
82
Short-Term Memory (LSTM) networks are trained using daily rainfall and 
            
83
temperature data, together with historic streamflow in an 
83
temperature data, together with historic streamflow in an 
            
84
autoregressive manner. The approach incorporates predicted streamflow 
84
autoregressive manner. The approach incorporates predicted streamflow 
            
85
values into the look-back window to generate continuous predictions 
85
values into the look-back window to generate continuous predictions 
            
86
across the testing period, enabling both gap-filling and short-term 
86
across the testing period, enabling both gap-filling and short-term 
            
87
forecasting. To ensure physically consistent outputs, the authors 
87
forecasting. To ensure physically consistent outputs, the authors 
            
88
modify standard GRU/LSTM architectures by (i) changing the activation 
88
modify standard GRU/LSTM architectures by (i) changing the activation 
            
89
function in the final hidden layer and (ii) applying a non-negative 
89
function in the final hidden layer and (ii) applying a non-negative 
            
90
constraint in the dense layer to prevent negative streamflow 
90
constraint in the dense layer to prevent negative streamflow 
            
91
simulations. Weather inputs are sourced from (1) an ARC station record 
91
simulations. Weather inputs are sourced from (1) an ARC station record 
            
92
and (2) gridded products (CHIRPS rainfall and NASA POWER/NASAP 
92
and (2) gridded products (CHIRPS rainfall and NASA POWER/NASAP 
            
93
variables), combined with DWS streamflow data from gauging stations 
93
variables), combined with DWS streamflow data from gauging stations 
            
94
B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and 
94
B4H007 and B4H001. Training spans 1 Oct 1979\u201330 Sep 1997 and 
            
95
testing spans 1 Oct 1997\u201322 Feb 2002. Results show that models 
95
testing spans 1 Oct 1997\u201322 Feb 2002. Results show that models 
            
96
using ARC station data achieve reliable predictions, while models 
96
using ARC station data achieve reliable predictions, while models 
            
97
using gridded datasets provide moderately accurate predictions, 
97
using gridded datasets provide moderately accurate predictions, 
            
98
indicating potential for low-cost streamflow estimation where gauges 
98
indicating potential for low-cost streamflow estimation where gauges 
            
99
are inactive or incomplete. The work contributes a practical method 
99
are inactive or incomplete. The work contributes a practical method 
            
100
for southern Africa where monitoring networks have declined.",
100
for southern Africa where monitoring networks have declined.",
            
101
  "num_resources": 4,
101
  "num_resources": 4,
            
102
  "num_tags": 0,
102
  "num_tags": 0,
            
103
  "organization": {
103
  "organization": {
            
104
    "approval_status": "approved",
104
    "approval_status": "approved",
            
105
    "created": "2026-02-19T10:16:04.079269",
105
    "created": "2026-02-19T10:16:04.079269",
            
106
    "description": "The University of Pretoria (UP) is one of 
106
    "description": "The University of Pretoria (UP) is one of 
            
107
Africa\u2019s top universities and the largest contact university in 
107
Africa\u2019s top universities and the largest contact university in 
            
108
South Africa. We produce socially impactful research to find solutions 
108
South Africa. We produce socially impactful research to find solutions 
            
109
for the world\u2019s most pressing issues. We have a high quality of 
109
for the world\u2019s most pressing issues. We have a high quality of 
            
110
teaching and learning in the classroom, online, or in communities. We 
110
teaching and learning in the classroom, online, or in communities. We 
            
111
have support in place for our students to graduate on time as 
111
have support in place for our students to graduate on time as 
            
112
well-rounded, responsible citizens fully prepared for the world beyond 
112
well-rounded, responsible citizens fully prepared for the world beyond 
            
113
university.",
113
university.",
            
114
    "id": "149918e1-73c3-463f-a6fe-401915e67e0f",
114
    "id": "149918e1-73c3-463f-a6fe-401915e67e0f",
            
115
    "image_url": "2026-02-19-101604.068497UPlogo.jpg",
115
    "image_url": "2026-02-19-101604.068497UPlogo.jpg",
            
116
    "is_organization": true,
116
    "is_organization": true,
            
117
    "name": "university-of-pretoria",
117
    "name": "university-of-pretoria",
            
118
    "state": "active",
118
    "state": "active",
            
119
    "title": "University of Pretoria",
119
    "title": "University of Pretoria",
            
120
    "type": "organization"
120
    "type": "organization"
            
121
  },
121
  },
            
122
  "owner_org": "149918e1-73c3-463f-a6fe-401915e67e0f",
122
  "owner_org": "149918e1-73c3-463f-a6fe-401915e67e0f",
            
123
  "private": false,
123
  "private": false,
            
124
  "publication_date": "2024-02-28",
124
  "publication_date": "2024-02-28",
            
125
  "publisher": "Journal of Hydroinformatics",
125
  "publisher": "Journal of Hydroinformatics",
            
126
  "relationships_as_object": [],
126
  "relationships_as_object": [],
            
127
  "relationships_as_subject": [],
127
  "relationships_as_subject": [],
            
128
  "resources": [
128
  "resources": [
            
129
    {
129
    {
            
130
      "cache_last_updated": null,
130
      "cache_last_updated": null,
            
131
      "cache_url": null,
131
      "cache_url": null,
            
132
      "created": "2025-10-22T11:55:59.688311",
132
      "created": "2025-10-22T11:55:59.688311",
            
133
      "description": null,
133
      "description": null,
            
134
      "download_count": 4,
134
      "download_count": 4,
            
135
      "format": "zip",
135
      "format": "zip",
            
136
      "hash": "",
136
      "hash": "",
            
137
      "id": "b865b864-11ed-4ba0-9a99-131e95cc7af1",
137
      "id": "b865b864-11ed-4ba0-9a99-131e95cc7af1",
            
138
      "is_data_supplementary": "False",
138
      "is_data_supplementary": "False",
            
139
      "last_modified": "2025-10-22T11:55:59.659251",
139
      "last_modified": "2025-10-22T11:55:59.659251",
            
140
      "metadata_modified": "2026-03-28T17:11:30.294112",
140
      "metadata_modified": "2026-03-28T17:11:30.294112",
            
141
      "mimetype": "application/zip",
141
      "mimetype": "application/zip",
            
142
      "mimetype_inner": null,
142
      "mimetype_inner": null,
            
143
      "name": "Data_and_Code-8669.zip",
143
      "name": "Data_and_Code-8669.zip",
            
144
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
144
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
            
145
      "position": 0,
145
      "position": 0,
            
146
      "resource_type": null,
146
      "resource_type": null,
            
147
      "size": 182163782,
147
      "size": 182163782,
            
148
      "state": "active",
148
      "state": "active",
            
149
      "url": 
149
      "url": 
            
150
b865b864-11ed-4ba0-9a99-131e95cc7af1/download/data_and_code-8669.zip",
150
b865b864-11ed-4ba0-9a99-131e95cc7af1/download/data_and_code-8669.zip",
            
151
      "url_type": "upload",
151
      "url_type": "upload",
            
152
      "zipped_file": "False"
152
      "zipped_file": "False"
            
153
    },
153
    },
            
154
    {
154
    {
            
155
      "cache_last_updated": null,
155
      "cache_last_updated": null,
            
156
      "cache_url": null,
156
      "cache_url": null,
            
157
      "created": "2025-10-22T11:56:00.990793",
157
      "created": "2025-10-22T11:56:00.990793",
            
158
      "description": null,
158
      "description": null,
            
159
      "download_count": 1,
159
      "download_count": 1,
            
160
      "format": "zip",
160
      "format": "zip",
            
161
      "hash": "",
161
      "hash": "",
            
162
      "id": "1ffcc4b6-01d5-4d53-9370-5ddd3d65b96b",
162
      "id": "1ffcc4b6-01d5-4d53-9370-5ddd3d65b96b",
            
163
      "is_data_supplementary": "False",
163
      "is_data_supplementary": "False",
            
164
      "last_modified": "2025-10-22T11:56:00.951126",
164
      "last_modified": "2025-10-22T11:56:00.951126",
            
165
      "metadata_modified": "2026-03-15T03:06:46.708421",
165
      "metadata_modified": "2026-03-15T03:06:46.708421",
            
166
      "mimetype": "application/zip",
166
      "mimetype": "application/zip",
            
167
      "mimetype_inner": null,
167
      "mimetype_inner": null,
            
168
      "name": "Data_and_Code/CatchmentA.zip",
168
      "name": "Data_and_Code/CatchmentA.zip",
            
169
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
169
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
            
170
      "position": 1,
170
      "position": 1,
            
171
      "resource_type": null,
171
      "resource_type": null,
            
172
      "size": 90861991,
172
      "size": 90861991,
            
173
      "state": "active",
173
      "state": "active",
            
174
      "url": 
174
      "url": 
            
175
esource/1ffcc4b6-01d5-4d53-9370-5ddd3d65b96b/download/catchmenta.zip",
175
esource/1ffcc4b6-01d5-4d53-9370-5ddd3d65b96b/download/catchmenta.zip",
            
176
      "url_type": "upload",
176
      "url_type": "upload",
            
177
      "zipped_file": "False"
177
      "zipped_file": "False"
            
178
    },
178
    },
            
179
    {
179
    {
            
180
      "cache_last_updated": null,
180
      "cache_last_updated": null,
            
181
      "cache_url": null,
181
      "cache_url": null,
            
182
      "created": "2025-10-22T11:56:01.279738",
182
      "created": "2025-10-22T11:56:01.279738",
            
183
      "description": null,
183
      "description": null,
            
n
184
      "download_count": 18,
n
184
      "download_count": 19,
            
185
      "format": "txt",
185
      "format": "txt",
            
186
      "hash": "",
186
      "hash": "",
            
187
      "id": "7b2380d0-2694-483c-99b8-215c25efd391",
187
      "id": "7b2380d0-2694-483c-99b8-215c25efd391",
            
188
      "is_data_supplementary": "False",
188
      "is_data_supplementary": "False",
            
189
      "last_modified": "2025-10-22T11:56:01.244307",
189
      "last_modified": "2025-10-22T11:56:01.244307",
            
n
190
      "metadata_modified": "2026-04-08T14:42:55.701963",
n
190
      "metadata_modified": "2026-04-09T03:36:22.247381",
            
191
      "mimetype": "text/plain",
191
      "mimetype": "text/plain",
            
192
      "mimetype_inner": null,
192
      "mimetype_inner": null,
            
193
      "name": "Data_and_Code/readMe.txt",
193
      "name": "Data_and_Code/readMe.txt",
            
194
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
194
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
            
195
      "position": 2,
195
      "position": 2,
            
196
      "resource_type": null,
196
      "resource_type": null,
            
197
      "size": 2437,
197
      "size": 2437,
            
198
      "state": "active",
198
      "state": "active",
            
199
      "url": 
199
      "url": 
            
200
d8/resource/7b2380d0-2694-483c-99b8-215c25efd391/download/readme.txt",
200
d8/resource/7b2380d0-2694-483c-99b8-215c25efd391/download/readme.txt",
            
201
      "url_type": "upload",
201
      "url_type": "upload",
            
202
      "zipped_file": "False"
202
      "zipped_file": "False"
            
203
    },
203
    },
            
204
    {
204
    {
            
205
      "cache_last_updated": null,
205
      "cache_last_updated": null,
            
206
      "cache_url": null,
206
      "cache_url": null,
            
207
      "created": "2025-10-22T11:56:01.522228",
207
      "created": "2025-10-22T11:56:01.522228",
            
208
      "description": null,
208
      "description": null,
            
209
      "download_count": 1,
209
      "download_count": 1,
            
210
      "format": "zip",
210
      "format": "zip",
            
211
      "hash": "",
211
      "hash": "",
            
212
      "id": "fce0c251-7c53-4b34-9183-5705dfd7e59c",
212
      "id": "fce0c251-7c53-4b34-9183-5705dfd7e59c",
            
213
      "is_data_supplementary": "False",
213
      "is_data_supplementary": "False",
            
214
      "last_modified": "2025-10-22T11:56:01.481064",
214
      "last_modified": "2025-10-22T11:56:01.481064",
            
215
      "metadata_modified": "2026-03-20T03:24:14.135999",
215
      "metadata_modified": "2026-03-20T03:24:14.135999",
            
216
      "mimetype": "application/zip",
216
      "mimetype": "application/zip",
            
217
      "mimetype_inner": null,
217
      "mimetype_inner": null,
            
218
      "name": "Data_and_Code/CatchmentB.zip",
218
      "name": "Data_and_Code/CatchmentB.zip",
            
219
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
219
      "package_id": "4d7601c4-5742-4e9c-93c6-73f8e5814dd8",
            
220
      "position": 3,
220
      "position": 3,
            
221
      "resource_type": null,
221
      "resource_type": null,
            
222
      "size": 91300326,
222
      "size": 91300326,
            
223
      "state": "active",
223
      "state": "active",
            
224
      "url": 
224
      "url": 
            
225
esource/fce0c251-7c53-4b34-9183-5705dfd7e59c/download/catchmentb.zip",
225
esource/fce0c251-7c53-4b34-9183-5705dfd7e59c/download/catchmentb.zip",
            
226
      "url_type": "upload",
226
      "url_type": "upload",
            
227
      "zipped_file": "False"
227
      "zipped_file": "False"
            
228
    }
228
    }
            
229
  ],
229
  ],
            
t
230
  "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[16.4699, 
t

            
231
-22.1265], [32.8931, -22.1265], [32.8931, -34.8212], [16.4699, 

            
232
-34.8212], [16.4699, -22.1265]]]}",

            
233
  "state": "active",
230
  "state": "active",
            
234
  "tags": [],
231
  "tags": [],
            
235
  "title": "Leveraging historic streamflow and weather data with deep 
232
  "title": "Leveraging historic streamflow and weather data with deep 
            
236
learning for enhanced streamflow predictions",
233
learning for enhanced streamflow predictions",
            
237
  "type": "dataset",
234
  "type": "dataset",
            
238
  "uploader_estimation_of_extent_of_processing": "access",
235
  "uploader_estimation_of_extent_of_processing": "access",
            
239
  "url": null,
236
  "url": null,
            
240
  "version": null,
237
  "version": null,
            
241
  "vertical_extent_datum": "masl",
238
  "vertical_extent_datum": "masl",
            
242
  "wrc_project_number": "WRC Project C2020/2021-00440",
239
  "wrc_project_number": "WRC Project C2020/2021-00440",
            
243
  "wro_theme": "streamflow"
240
  "wro_theme": "streamflow"
            
244
}
241
}