Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? - A proof-of-concept study
Copyright © 2024 Elsevier B.V. All rights reserved..
Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:924 |
|---|---|
| Enthalten in: |
The Science of the total environment - 924(2024) vom: 10. Apr., Seite 171365 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Yang, Guangyu Gary [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Revised 02.04.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1016/j.scitotenv.2024.171365 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369481836 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369481836 | ||
| 003 | DE-627 | ||
| 005 | 20240403000421.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240309s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.scitotenv.2024.171365 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1361.xml |
| 035 | |a (DE-627)NLM369481836 | ||
| 035 | |a (NLM)38458452 | ||
| 035 | |a (PII)S0048-9697(24)01505-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Yang, Guangyu Gary |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? - A proof-of-concept study |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 02.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier B.V. All rights reserved. | ||
| 520 | |a Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Indian Ocean | |
| 650 | 4 | |a Nitrate | |
| 650 | 4 | |a Three-dimensional reconstruction | |
| 700 | 1 | |a Wang, Qishuo |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Jiacheng |e verfasserin |4 aut | |
| 700 | 1 | |a He, Lechi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Rongzu |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Wenfang |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Enhui |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Zhigang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The Science of the total environment |d 1972 |g 924(2024) vom: 10. Apr., Seite 171365 |w (DE-627)NLM000215562 |x 1879-1026 |7 nnns |
| 773 | 1 | 8 | |g volume:924 |g year:2024 |g day:10 |g month:04 |g pages:171365 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.scitotenv.2024.171365 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 924 |j 2024 |b 10 |c 04 |h 171365 | ||