1
|
Mohammadi-Raigani Z, Gholami H, Mohamadifar A, Samani AN, Pradhan B. Using an interpretable deep learning model for the prediction of riverine suspended sediment load. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:32480-32493. [PMID: 38656723 DOI: 10.1007/s11356-024-33290-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.
Collapse
Affiliation(s)
- Zeinab Mohammadi-Raigani
- Department of Natural Resources Engineering, University of Hormozgan, Bandar‑Abbas, Hormozgan, Iran
| | - Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar‑Abbas, Hormozgan, Iran.
| | - Aliakbar Mohamadifar
- Department of Natural Resources Engineering, University of Hormozgan, Bandar‑Abbas, Hormozgan, Iran
| | - Aliakbar Nazari Samani
- Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Tehran, Iran
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia
- Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, UKM Bangi, 43600, Selangor, Malaysia
| |
Collapse
|
2
|
Collins AL, Blackwell M, Boeckx P, Chivers CA, Emelko M, Evrard O, Foster I, Gellis A, Gholami H, Granger S, Harris P, Horowitz AJ, Laceby JP, Martinez-Carreras N, Minella J, Mol L, Nosrati K, Pulley S, Silins U, da Silva YJ, Stone M, Tiecher T, Upadhayay HR, Zhang Y. Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes. JOURNAL OF SOILS AND SEDIMENTS 2020; 20:4160-4193. [PMID: 33239964 PMCID: PMC7679299 DOI: 10.1007/s11368-020-02755-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/13/2020] [Indexed: 05/23/2023]
Abstract
PURPOSE This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science. METHODS Web of Science and Google Scholar were used to review published papers spanning the period 2013-2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018-2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017. SCOPE Areas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing. CONCLUSIONS The popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.
Collapse
Affiliation(s)
- Adrian L. Collins
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Martin Blackwell
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Pascal Boeckx
- Isotope Bioscience Laboratory-ISOFYS, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Charlotte-Anne Chivers
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
- Centre for Rural Policy Research, University of Exeter, Lazenby House, Pennsylvania Road, Exeter, EX4 4PJ UK
| | - Monica Emelko
- Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario Canada
| | - Olivier Evrard
- Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL), Unité Mixte de Recherche 8212 (CEA/CNRS/UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette Cedex, France
| | - Ian Foster
- Environmental & Geographical Sciences, Learning Hub (Room 101), University of Northampton, University Drive, Northampton, NN1 5PH UK
| | - Allen Gellis
- U.S. Geological Survey, 5522 Research Park Drive, Baltimore, MD 21228 USA
| | - Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan Iran
| | - Steve Granger
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Paul Harris
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Arthur J. Horowitz
- South Atlantic Water Science Center, U.S. Geological Survey, Atlanta, GA USA
| | - J. Patrick Laceby
- Alberta Environment and Parks, 3535 Research Rd NW, Calgary, Alberta T2L 2K8 Canada
| | - Nuria Martinez-Carreras
- Luxembourg Institute of Science and Technology (LIST), Catchment and Eco-hydrology Research Group (CAT), L-4422 Belvaux, Luxembourg
| | - Jean Minella
- Department of Soil Science, Federal University of Santa Maria, Roraima Ave. 1000, Santa Maria, RS 97105-900 Brazil
| | - Lisa Mol
- Department of Geography and Environmental Management, University of the West of England, Bristol, UK
| | - Kazem Nosrati
- Department of Physical Geography, School of Earth Sciences, Shahid Beheshti University, Tehran, 1983969411 Iran
| | - Simon Pulley
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Uldis Silins
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta T6G 2I7 Canada
| | - Yuri Jacques da Silva
- Agronomy Department, Federal University of Piaui (UFPI), Planalto Horizonte, Bom Jesus, PI 64900-000 Brazil
| | - Micheal Stone
- Department of Geography and Environmental Management, Faculty of Environment, University of Waterloo, EV1 Room 112, Waterloo, Canada
| | - Tales Tiecher
- Department of Soil Science, Federal University of Rio Grande do Sul, Bento Gonçalves Ave. 7712, Porto Alegre, RS 91540-000 Brazil
| | - Hari Ram Upadhayay
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| | - Yusheng Zhang
- Sustainable Agriculture Sciences, Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB UK
| |
Collapse
|