Time series forecasting of chlorophyll-a concentrations in the Chesapeake Bay.

Sahil Gupta, Sagar Gupta

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Advancing Near-Term Water Quality Forecasting with Explainable Machine Learning and Probabilistic Uncertainty Quantification.ACS ES&T water · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sahil GuptaBrown University, Providence, USA. sahil_gupta@brown.edu.

Sagar GuptaGeorgia Institute of Technology, Atlanta, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Declining water quality poses serious environmental and public health risks, with chlorophyll-a serving as a key biological indicator of harmful algal blooms. This study evaluates the use of a Long Short-Term Memory (LSTM) neural network to forecast chlorophyll-a concentrations in the Chesapeake Bay, a critical estuarine ecosystem supporting over 17 million people. Weekly satellite-derived chlorophyll-a measurements from 1997 to 2020 were collected for three geographic regions of the bay. The LSTM model was benchmarked against traditional statistical models, including ARIMA and TBATS, and trained to capture complex seasonal patterns in the time series data. Across all three regions, the LSTM consistently outperformed the other models, achieving lower root mean squared error (RMSE) values of 0.121, 0.155, and 0.199 mg/m

Indexed as

BaysChlorophyllChlorophyll AEnvironmental MonitoringEcosystemForecastingHarmful Algal BloomNeural Networks, ComputerSeasonsWater QualityChlorophyllChlorophyll AChesapeake bayChlorophyll-aLong short term memory (LSTM)Machine learningTime series forecastingWater quality management

Identifiers

PMID40846738

DOI10.1038/s41598-025-16352-3

PMCPMC12373891

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.