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ARTICLE PREVIEW

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 * Cited by (7)


SCIENCE OF THE TOTAL ENVIRONMENT

Volume 898, 10 November 2023, 165504




REMOTE SENSING-ENABLED MACHINE LEARNING FOR RIVER WATER QUALITY MODELING UNDER
MULTIDIMENSIONAL UNCERTAINTY

Author links open overlay panelSaiful Haque Rahat a, Todd Steissberg b, Won
Chang c, Xi Chen d, Garima Mandavya e, Jacob Tracy e, Asphota Wasti e, Gaurav
Atreya e, Shah Saki f, Md Abul Ehsan Bhuiyan g, Patrick Ray e
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HIGHLIGHTS

 * •
   
   Development of a model to evaluate climate change impacts on river water
   quality.

 * •
   
   Gaps in local water quality data were filled using remote-sensed satellite
   data.

 * •
   
   A machine learning model, trained on extensive data, assessed river water
   quality.

 * •
   
   The Monte Carlo dropout technique was applied to reduce prediction
   uncertainties.

 * •
   
   Constructed a generalizable method to detect spatial variability in water
   quality.




ABSTRACT

Two fundamental problems have inhibited progress in the simulation of river
water quality under climate (and other) uncertainty: 1) insufficient data, and
2) the inability of existing models to account for the complexity of factors
(e.g., hydro-climatic, basin characteristics, land use features) affecting river
water quality. To address these concerns this study presents a technique for
augmenting limited ground-based observations of water quality variables with
remote-sensed surface reflectance data by leveraging a machine learning model
capable of accommodating the multidimensionality of water quality influences.
Total Suspended Solids (TSS) can serve as a surrogate for chemical and
biological pollutants of concern in surface water bodies. Historically, TSS data
collection in the United States has been limited to the location of water
treatment plants where state or federal agencies conduct regularly-scheduled
water sampling. Mathematical models relating riverine TSS concentration to the
explanatory factors have therefore been limited and the relationships between
climate extremes and water contamination events have not been effectively
diagnosed. This paper presents a method to identify these issues by utilizing a
Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution
Imaging Spectroradiometer (MODIS) satellite reflectance data, which is
calibrated to TSS data collected by the Ohio River Valley Water Sanitation
Commission (ORSANCO). The methodology developed enables a thorough empirical
analysis and data-driven algorithms able to account for spatial variability
within the watershed and provide effective water quality prediction under
uncertainty.


GRAPHICAL ABSTRACT

 1. Download: Download high-res image (114KB)
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INTRODUCTION

Declining water quality is now an issue of global concern, affecting populations
even with a relative abundance of surface water resources. The most prevalent
problem is eutrophication, with acute chemical spills and chronic sewer-borne
flows of pharmaceuticals also presenting mounting health hazards (UNEP, 2016).
While the state of the art in water systems decision science has advanced
rapidly in the past 10–15 years, water quality has not received proportional
attention (Ho and Michalak, 2020; Michalak, 2016; Ray et al., 2020). Given the
link between climate extremes and water quality, an understanding of climate
change implications for water quality is critical for the future of water
quality management.

Michalak (2016) called for an investigation into the impact of climate change on
water quality, factoring in land use, population distributions, and other
regional factors that compound the effects of weather. Changes in these factors,
in combination with evolving flow regulation strategies and watershed
development, are magnifying floods throughout the United States (Vogel et al.,
2011), Europe (Fischer and Knutti, 2016), and likely elsewhere. At the same
time, worsening droughts in the American West (Williams et al., 2015) and
Southern Europe (Vicente-Serrano et al., 2014) are attributable at least in part
to anthropogenic global warming. Projections for mid-century indicate a high
likelihood of increased precipitation extremes with shifting seasonality and
reductions in winter snowpack altering the timing and magnitude of peak
streamflow outside of the design range of existing infrastructure and its
operating rules (Marchau et al., 2019; Miura et al., 2021; Ray and Brown, 2015;
Ryberg et al., 2014).

Additionally, studies on the covariances between hydro-climatic factors and
water quality parameters have provided insights into the impacts of human
activities and climate change on aquatic ecosystems (McCarthy et al., 2018;
Zonta et al., 2005). Anthropogenic activities have been found to negatively
impact water quality in both rural and urban areas (Ballard et al., 2019;
Mahdiyan et al., 2021). Temperature, as a result of climate change, has been
identified as a significant factor contributing to the proliferation of harmful
algal blooms in rivers (Motew et al., 2019; Ockenden et al., 2017). Total
Suspended Solid (TSS) and turbidity are commonly used indicators of water
contamination (Kutser, 2004; Swain and Sahoo, 2017; Yao et al., 2020). TSS
concentrations have been found to be highly correlated with various pollutants,
including heavy metals (Fichot et al., 2016; Nasrabadi et al., 2016), polycyclic
aromatic hydrocarbons (PAHs) (Rugner et al., 2013; Rugner et al., 2019),
polychlorinated biphenyls (PCBs) (Rugner et al., 2019), and E. coli microbial
concentrations (Bilotta and Brazier, 2008). High TSS concentrations can lead to
increased costs of water treatment, damage to in-stream infrastructure,
degradation of aquatic environments, and loss of reservoir storage capacity
(Arthington et al., 2010; Camara et al., 2019; Guo et al., 2015).

Historical data analysis has shown that TSS can be a useful supplemental
parameter for assessing water quality (Mukundan et al., 2018; Samal et al.,
2013; Wang et al., 2010). However, such assessments have only been possible in
locations with large repositories of historical TSS observations, such as at
drinking water intakes in New York City (Wang et al., 2010), Portland, Oregon
(Towler et al., 2010), and Japan (Fan and Shibata, 2015). Fortunately, TSS and
turbidity are widely measured and can be easily substituted for each other (Fan
and Shibata, 2015). Turbidity is commonly monitored at drinking water treatment
plant intakes, either on a daily basis or continuously. In regions with limited
data availability, remote sensing measurements can be used to derive turbidity,
which may act as a substitute for other water quality parameters (Gholizadeh et
al., 2016; Griffin et al., 2011; Sheffield et al., 2018).

Previous studies assessing water quality responses to climate variability have
generally focused on local scales, measuring only a limited range of
contaminants and using site-specific models (Barnard et al., 2021; Deng, 2019;
Ho and Michalak, 2020; Ranatunga et al., 2017). However, these approaches are
inadequate for understanding the impacts of droughts and floods on water quality
at larger scales. Physically-based models of river water quality have also been
limited by their ability to represent complex human-hydrological processes
through mathematical approximations (Alam et al., 2022; Clark et al., 2017; Fu
et al., 2020). In contrast, there has been development in data-driven machine
learning models. For instance, Zounemat-Kermani et al. (2021) employed
multilayer perception neural networks (MLPANNs) to identify riverine turbidity
and again, utilized adaptive neuro-fuzzy inference systems (ANFIS) to detect
groundwater quality (Zounemat-Kermani et al., 2022). Furthermore, Saha et al.
(2023) and Wang et al. (2023) incorporated deep learning techniques for river
water quality prediction. Despite these notable developments in machine learning
models, there are currently challenges in incorporating process-informed
knowledge related to the fundamental natural mechanisms that govern water
quality assessment on a larger scale (Bi et al., 2023; Khullar and Singh, 2022).

Furthermore, conventional water quality monitoring methods are characterized by
lengthy procedures that require in-situ measurements, sampling, and laboratory
analysis, rendering the production of consistent records of observations a
challenging task (Brightbill and Munn, 2008; Moreno-Madrinan et al., 2010; Petus
et al., 2010; Rouf et al., 2021). This variability in water quality parameters
can also be influenced by factors such as tides, winds, land use changes, and
human activities, which have not been fully accounted for in previous studies
(Brightbill and Munn, 2008; Moreno-Madrinan et al., 2010; Petus et al., 2010;
Rouf et al., 2021). Thus, the challenges to understand the effect of climate
extremes on water quality can be attributed to 1) a lack of comprehensive
spatio-temporal river water quality data (Kirschke et al., 2020; Peletz et al.,
2018); and 2) an inadequate understanding of the drivers of water quality from
climatic variables (Bhateria and Jain, 2016; Xu et al., 2021; Zhi et al., 2021)
and the risks influenced by anthropogenic activities such as land-use changes
and urbanization (Brackins et al., 2021; Dollan et al., 2022; Tamaddun et al.,
2019).

Keeping this in mind, the present study aimed to develop a generalizable
analytical approach for simulating river water quality by leveraging machine
learning techniques and remotely sensed water quality data to model the impacts
of changes in climate and other relevant factors, informed by a systematic
multidimensional risk assessment framework. To accomplish this, a Long Short
Term Memory (LSTM) model was trained on large datasets of water quality
indicators and explanatory variables to make the model process-informed.
Contrary to the conventional data-driven approach discussed, the model was
integrated into a workflow capable of accounting for spatial variations within
the watershed, including factors such as Land Use Changes, Basin
Characteristics, and Weather Variables, which may be affected by potential
future changes in climate, hydrology, hydraulics, and water system
infrastructure operation. In addition, the proposed methodology utilizes a Monte
Carlo-based dropout technique to generate visual representations of predictive
uncertainty from multiple model runs. For pilot demonstration purposes, the
methodology was applied to the Ohio River. To the best of our knowledge, this
approach is a first-of-its-kind unified water quality risk management framework
for large-scale river systems, which can be used by local utilities to manage
river contamination risks for emergency response and long-term planning.
Furthermore, it will serve as a valuable resource for scientists investigating
river water contamination events.


SECTION SNIPPETS


METHODS

The methodology developed for this study consists of three parts: 1) conversion
of satellite-based observations of surface reflectance to TSS; 2) spatial and
temporal expansion of the remote-sensed TSS beyond the sparse (bi-weekly, at
highly disjoint locations) training set; and 3) development of an LSTM regional
model for the Ohio River and prediction of water quality variables at ungauged
stations throughout the period of the historical record. A schematic
representation of the LSTM-based


RESULTS AND DISCUSSION

The results of this study are presented in two sections. The first section
presents the modeled correlation between the MODIS surface reflectance data and
bi-monthly observations of TSS to build a daily TSS database for the study area.
The second section summarizes the performance of the LSTM model for each station
and discusses the predicted outputs for temporal and spatial variability within
the watershed.


SUMMARY AND CONCLUSION

Water quality deterioration is a global challenge caused by climate change,
demographic conditions, land use change, and urbanization (Jung and Chang, 2012;
Wang et al., 2021). However, the development of critical decision-relevant
information on river water quality is impeded by fundamental problems, such as
insufficient data and the inability of existing models to account for the
complexity of factors influencing water quality (Ahmed et al., 2020; Nijhawan
and Howard, 2022; Uddin et al., 2021


CREDIT AUTHORSHIP CONTRIBUTION STATEMENT

Saiful Haque Rahat: Conceptualization, Methodology, Data Collection, Analysis
and development, Writing - original draft, Visualization, Script writing;

Todd Steissberg: Writing - review & editing, Supervision;

Won Chang: Writing - review & editing, Supervision;

Xi Chen: Writing - review & editing, Supervision;

Garima Mandavya: Data Collection, Visualization, Writing-review & editing;

Jacob Tracy: Script Writing, Conceptualization;

Asphota Wasti: Data Collection, Script Writing, Conceptualization,


DECLARATION OF COMPETING INTEREST

The authors declare the following financial interests/personal relationships
which may be considered as potential competing interests: Saiful Haque Rahat
reports financial support was provided by US Army Corps of Engineers. Patrick
Ray reports financial support was provided by US Army Corps of Engineers. Saiful
Haque Rahat reports financial support was provided by University of Cincinnati.
Patrick Ray reports financial support was provided by University of Cincinnati.
Saiful Haque Rahat reports


ACKNOWLEDGMENTS

This work was funded by the US Army Corps of Engineers (contract
IPA000-20-0-0004) and the University of Cincinnati Research Council (Internal
Order #R25095). The authors acknowledge the support from our collaborators at
the US Army Corps of Engineers (USACE) and the National Oceanic and Atmospheric
Administration (NOAA), for their helpful and encouraging contribution throughout
the development of this study.

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   the near future; (4) Dam construction's impact on turbidity was not
   significant. Compared with natural factors (permanent wetlands, grasslands,
   lake surface water temperature, and remote sensing ecological index), human
   activities (barren, urban and built-up lands, croplands and population
   density) had a more significant impact on turbidity. Turbidity was highly
   correlated with croplands (r = 0.76), followed by population density
   (r = 0.71), and urban and built-up lands (r = 0.69).


 * ESTIMATING THE INFLUENCE OF WATER CONTROL INFRASTRUCTURE ON NATURAL LOW FLOW
   IN COMPLEX RESERVOIR SYSTEMS: A CASE STUDY OF THE OHIO RIVER
   
   2024, Journal of Hydrology: Regional Studies
   Show abstract
   
   The Ohio River in the northeast United States (US).
   
   Low streamflows are critical for urban water security, agricultural
   irrigation, water quality regulatory thresholds, navigation passage, and
   ecological well-being. However, there is insufficient understanding of the
   natural low flow conditions in rivers containing dams and artificial
   reservoirs, in part because we have inadequate records of natural flows prior
   to the introduction of the water control infrastructure. We demonstrate an
   improved technique for estimation of human impact on low flow, and describe
   the analytical innovations necessary to apply the technique. The primary
   innovation necessary was the development of a parsimonious streamflow routing
   algorithm to aggregate naturalized flow from reservoirs operated by the US
   Army Corps of Engineers (USACE) to locations of concern along the Ohio River
   mainstem.
   
   This study shows that, in dry years, releases from USACE reservoirs during
   autumn months account for up to approximately half of the mainstem flow, and
   the influence of USACE water control infrastructure is more pronounced on
   many of the Ohio River’s tributaries. The Flow Duration Curves also show
   significant differences in low flow throughout the mainstem. This has
   implications for dry season river functionality in all the categories listed
   above; if the infrastructure were to fail, or lose effectiveness due to
   climate change, these river functions would be threatened.


 * A QUALITATIVE STUDY OF WATER QUALITY USING LANDSAT 8 AND STATION WATER
   QUALITY-MONITORING DATA TO SUPPORT SDG 6.3.2 EVALUATIONS: A CASE STUDY OF
   DEQING, CHINA
   
   2024, Water (Switzerland)
   
   


 * MONITORING THE WATER QUALITY DISTRIBUTION CHARACTERISTICS IN THE HUAIHE RIVER
   BASIN BASED ON THE SENTINEL-2 SATELLITE
   
   2024, Water (Switzerland)
   
   


 * UNCERTAINTY ANALYSIS OF RIVER WATER QUALITY BASED ON STOCHASTIC OPTIMIZATION
   OF WASTE LOAD ALLOCATION USING THE GENERALIZED LIKELIHOOD UNCERTAINTY
   ESTIMATION METHOD
   
   2024, Water Resources Management
   
   


 * AN UPDATE TO PROBABLE MAXIMUM PRECIPITATION ACCOUNTING FOR MORE THAN MOISTURE
   AVAILABILITY ALONE: A CASE STUDY OF NEPAL.
   
   2023, Research Square
   
   

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