An enhanced deep learning approach to assessing inland water quality and the affecting factors using Landsat 8 and Sentinel-2
Bibliographic record
Abstract
The estimation of water quality parameters (WQPs) using remote sensing is difficult due to the complex correlation between WQPs and water optical properties, the interactions of WQPs, and the impacts of climate. We proposed enhanced multimodal deep learning (EMDL) models for Chlorophyll-a (Chla), total phosphorous (TP), total nitrogen (TN), Secchi disk depth (SDD), dissolved organic carbon (DOC), and dissolved oxygen (DO) estimation in Lake Simcoe, Canada. The EMDL models were developed and validated using the remote sensing reflectance derived from the harmonized Landsat and Sentinel-2 images, synchronized in-situ water quality measurements, water surface temperature, and climate data (N = 950). Using the EMDL models, the spatiotemporal water quality patterns of Lake Simcoe from 2013 to 2019 were reconstructed. Besides, we quantitatively analyzed the impacts of 12 potential natural and anthropogenic factors on the water quality of Lake Simcoe. The results showed that the EMDL models had the potential to detect the spatiotemporal dynamics of water quality with the Slope being close to 1 (0.84−0.95), normalized mean absolute error ≤ 20.17%, and Bias ≤ 14.68%. Human activities such as urban development and agricultural activities mainly affected the water quality of Lake Simcoe. This study provides a practical approach to supporting the environmental management of regional inland watersheds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".