In high-altitude trout raceways, dissolved oxygen (DO) declines are brief and start from a narrow margin below saturation. We built a solar-powered floating node with five water-quality channels, a serviceable probe cartridge, switched sensor rails a...
Time-series water-quality forecasting plays a crucial role in sustainable environmental monitoring, early-warning surveillance, and data-driven water-resource governance. Degradation of river ecosystems poses significant risks to public health, biodi...
Rapid population growth and climate change have created challenges for managing water quality. Protecting water sources and devising practical solutions are essential for restoring impaired inland rivers. Traditional water quality monitoring and fore...
Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodie...
Nowadays, the effects of water pollution accidents on water quality safety and regional residents’ living have attracted worldwide attention. Therefore, the objective of this research is to propose an early warning and forecasting model and develop a...
Real-time, reliable forecasting of water quality (WQ) is a critical component of sustainable environmental management. A key challenge, however, is modeling time-varying uncertainty (heteroscedasticity), particularly during disruptive events like sto...
Aquaponic systems require reliable forecasting of water quality parameters to support proactive monitoring and maintain suitable conditions for aquatic organisms. This study investigates Transformer-based deep learning for multi-step, multi-target wa...
Graph neural networks are increasingly used for river water-quality forecasting because they can represent interactions among monitoring locations that conventional site-wise time-series models cannot capture. However, the usefulness of graph learnin...
Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data is a key strategy in the field of water quality monitoring and t...
We developed an artificial neural network (ANN)-based water quality prediction model and evaluated the applicability of the model using regional probability forecasts provided by the Korea Meteorological Administration as the input data of the model....
Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importanc...
Water quality time series exhibit multi-scale periodicities, including daily and weekly cycles, driven by solar radiation, tidal forcing, and seasonal variation. Existing deep learning methods typically rely on patch-based attention or adaptive perio...
The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines S...
There is a lack of a systematic comparison framework that can assess models in both single-step and multi-step forecasting situations while balancing accuracy, training efficiency, and prediction horizon. This study aims to evaluate the predictive ca...
Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource alloc...
Chlorophyll-a concentration (Chl-a) is a key indicator of marine water quality, phytoplankton biomass, and aquatic ecosystem status. Multi-step forecasting is challenging because Chl-a dynamics exhibit regional heterogeneity, multi-scale variability,...
High-altitude lakes are increasingly exposed to nutrient enrichment, organic loading and wastewater-derived contamination, while predictive performance can be overstated when temporally ordered observations are randomly partitioned or target-defining...
Water quality prediction serves as an important foundation for risk control and the proactive management of the aquatic environment, and the Long Short-Term Memory (LSTM) network has gained recognition as an effective approach for achieving high-prec...
Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network th...
The monitoring and prediction of water quality parameters are important tasks in the management of water resources. In this work, the performances of time series statistical models were evaluated to predict and forecast the dissolved oxygen (DO) conc...
This study presents the implementation and evaluation of a high-resolution operational forecasting system for the Tagus estuary (Portugal), focusing on inundation and water quality predictions to support estuarine management. Developed using the relo...
Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distr...
Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale...
The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatme...
Forecasting of water quality is crucial for safeguarding public health, optimizing resource management, and promoting environmental sustainability. However, predicting water quality dynamics is challenging due to nonlinear relationships among environ...
The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed durin...
The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aim...
Water quality is crucial to the economy and ecology because a healthy aquatic eco-system supports human survival and biodiversity. We have developed the Neuro-Adapt Fuzzy Strategist (NAFS) to improve water quality index (WQI) forecasting accuracy. Th...
Water quality has significantly declined over the past few decades due to high industrial rates, rapid urbanization, anthropogenic activities, and inappropriate rubbish disposal in Lake Tanganyika. Consequently, forecasting water quantity and quality...
Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality foreca...
To control the quality of mineral-medicinal waters and ensure their therapeutic benefits, spas often rely on periodic discrete sampling to analyze the physico-chemical properties of their pools. The AQUAPRED project aims to digitize this process by d...
To achieve a more accurate assessment of water resource carrying capacity (WRCC), the indicators of water resources, social resources, and ecological environment were selected to construct the WRCC system on the basis of the combinatorial assignment...
Water is a fundamental resource for human survival but the consumption of water that is unfit for drinking leads to serious diseases. Access to high–resolution satellite imagery provides an opportunity for innovation in the techniques used for...
Groundwater is regarded as the primary source of agricultural and drinking water in semi-arid and arid regions. However, toxic substances released from sources such as landfills, industries, insecticides, and fertilizers from the previous year exhibi...
High-frequency Doppler monitoring can support operational water-resource assessment in semi-arid and irrigation-dependent rivers, but existing sensor-to-forecast workflows often address data quality control, temporal diagnostics, and forecasting as s...
The Mar Menor is a coastal lagoon of great socio-ecological and environmental value; in recent years, different localized episodes of hypoxia and eutrophication have modified the quality of its waters. The episodes are due to a drop in dissolved oxyg...
Coastal ecosystems are facing critical water quality deterioration, while the most convenient passage to the South China Sea, Beibu Gulf, has been under considerable pressure to its ecological environment due to rapid development and urbanization. In...
European Union (EU) policy encourages the development of a blue economy (BE) by unlocking the full economic potential of oceans, seas, lakes, rivers and other water resources, especially in member countries in which it represents a low contribution t...
Water quality management in large-scale institutional infrastructures faces significant challenges due to the high latency of manual sampling and the energy–connectivity trade-offs in traditional IoT deployments. This paper proposes HydroSense...
Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an...
Freshwater quality is degrading globally, and regulatory monitoring remains largely retrospective, identifying non-compliance only after it occurs. This study identifies whether multi-year, regulatorily relevant compliance breaches can be forecast fr...
Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting mo...
The World Health Organization has estimated the burden of the on-going pandemic of cholera at 1.3 to 4 million cases per year worldwide in 2016, and a doubling of case-fatality-rate to 1.8% in 2016 from 0.8% in 2015. The disease cholera is caused by...
Surface water systems face unprecedented stress due to climate variability, urbanization, land-use change, and growing water demand—prompting a shift from traditional hydrological modeling to intelligent, adaptive systems. This review critically expl...
Different from the traditional irrigation optimization model based only on the water production function, in this study, we explored the water–yield–quality–benefit relationship and established a general irrigation scheduling optimi...
The Kelani River is one of the most important freshwater resources in Sri Lanka, supplying drinking water, supporting aquatic ecosystems, and sustaining industrial and agricultural activities. However, increasing anthropogenic pressures have intensif...
Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span...
Dissolved oxygen (DO) dynamics in river systems exhibit complex nonlinear and non-stationary characteristics driven by interactions among meteorological and water-quality factors, posing challenges for accurate prediction and identification of domina...
This research evaluates water quality in two contrasting hydro-climatic regions: the River Liffey in Ireland and the Andarax River in Spain. It utilizes an Artificial Neural Network (ANN) to simulate potential changes in key water-quality parameters...
Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaus...
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