Comprehensive Assessment of E. coli Dynamics in River Water Using Advanced Machine Learning and Explainable AI

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 106816 - 106816

Published: Jan. 1, 2025

Language: Английский

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An IoT based smart water quality assessment framework for aqua-ponds management using Dilated Spatial-temporal Convolution Neural Network (DSTCNN)

Aquacultural Engineering, Journal Year: 2023, Volume and Issue: 104, P. 102373 - 102373

Published: Nov. 10, 2023

Language: Английский

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Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 351, P. 119866 - 119866

Published: Dec. 25, 2023

Language: Английский

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A critical analysis of parameter choices in water quality assessment

Water Research, Journal Year: 2024, Volume and Issue: 258, P. 121777 - 121777

Published: May 16, 2024

The determination of water quality heavily depends on the selection parameters recorded from samples for index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine parameter set four main reasons: reducing cost uncertainty, addressing eclipsing problem, enhancing performance predicting WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies this area. Such essential assess validity these objectives demonstrate effectiveness data-driven methods achieving goals. This paper sets out with two primary aims: first, provide review existing literature selecting parameters. Second, it seeks delineate evaluate principal motivations identified literature. manuscript categorizes into methodological groups refining parameters: one focuses preserving information within dataset, another ensures consistent prediction using full It characterizes each group evaluates how effectively approach meets predefined objectives. study presents minimal WQI approach, common both categories, only has successfully reduced recording costs. Nonetheless, notes simply number does not guarantee savings. Furthermore, classified as dataset demonstrated potential decrease whereas have been able mitigate issue. Additionally, since approaches still rely initial chosen by experts, they do eliminate need expert judgment. further points formula straightforward expedient tool assessing quality. Consequently, argues employing solely reduce enhance standalone solution. Rather, objective should be integrated more research critical analysis characterization lay groundwork future research. will enable subsequent proposed can achieve

Language: Английский

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Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

Water Research X, Journal Year: 2024, Volume and Issue: 23, P. 100228 - 100228

Published: May 1, 2024

The impacts of climate change on hydrology underscore the urgency understanding watershed hydrological patterns for sustainable water resource management. conventional physics-based fully distributed models are limited due to computational demands, particularly in case large-scale watersheds. Deep learning (DL) offers a promising solution handling large datasets and extracting intricate data relationships. Here, we propose DL modeling framework, incorporating convolutional neural networks (CNNs) efficiently replicate model outputs at high spatial resolution. goal was estimate groundwater head surface depth Sabgyo Stream Watershed, South Korea. consisted input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, initial conditions. conditions target were obtained from HydroGeoSphere (HGS), whereas other inputs actual measurements field. By optimizing training sample size, design, CNN structure, hyperparameters, found that CNNs with residual architectures (ResNets) yielded superior performance. optimal reduces computation time by 45 times compared HGS monthly estimations over five years (RMSE 2.35 0.29 m water, respectively). In addition, our framework explored predictive capabilities responses future scenarios. Although proposed is cost-effective simulations, further enhancements needed improve accuracy long-term predictions. Ultimately, has potential facilitate decision-making, complex

Language: Английский

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The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(15), P. 6103 - 6119

Published: Aug. 9, 2024

Language: Английский

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Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3380 - 3380

Published: Nov. 24, 2024

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance accuracy, speed, accessibility monitoring. Data collected from various samples in Algeria were analyzed determine key such as conductivity, turbidity, pH, total dissolved solids (TDS). These measurements integrated into deep neural networks (DNNs) predict indices sodium adsorption ratio (SAR), magnesium hazard (MH), percentage (SP), Kelley’s (KR), potential salinity (PS), exchangeable (ESP), well Water Quality Index (WQI) Irrigation (IWQI). DNNs model, optimized through selection activation functions hidden layers, demonstrated high precision, with a correlation coefficient (R) 0.9994 low root mean square error (RMSE) 0.0020. AI-driven methodology significantly reduces reliance traditional laboratory analyses, offering real-time assessments that are adaptable local conditions environmentally sustainable. provides practical solution resource managers, particularly resource-limited regions, efficiently monitor make informed decisions public health agricultural applications.

Language: Английский

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Water contamination analysis in IoT enabled aquaculture using deep learning based AODEGRU

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102405 - 102405

Published: Dec. 12, 2023

Water contamination presents a significant challenge in aquaculture, impacting the sustainability of ecosystems and health aquatic organisms. Precisely assessing water levels is crucial for effective monitoring safeguarding life within aquaculture industry. Traditional methods evaluating are characterized by their costliness, time-consuming nature, susceptibility to errors. Integrating computer technologies such as Artificial Intelligence (AI), Internet Things (IoT), Data Analytics offers promising potential addressing this issue. Nevertheless, current deep learning solutions have limitations related data variability, interpretability, performance. To address these limitations, study proposes comprehensive framework that incorporates IoT-based collection segregation techniques enhance accuracy classification aquaculture. Real-time collected through IoT devices, encompassing parameters like temperature, pH levels, dissolved oxygen, nitrate concentration, other quality indicators, enables holistic evaluation quality. By considering predefined acceptable ranges life, calculates index, facilitating into categories contaminated non-contaminated. ensure robust classification, introduces an innovative attention-based model known Ordinary Differential Equation Gated Recurrent Unit (AODEGRU). This attention mechanism directs model's focus towards salient features associated with contamination, while AODEGRU architecture captures temporal patterns data. Experimental results underscore effectiveness proposed model. It demonstrates its superiority high performance, achieving rate approximately 98.69% on publicly available dataset impressive 99.89% real-time dataset, clearly outperforming existing methodologies.

Language: Английский

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Mapping groundwater potentiality by using hybrid machine learning models under the scenario of climate variability: a national level study of Bangladesh

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: March 29, 2024

Language: Английский

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Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(30), P. 42948 - 42969

Published: June 17, 2024

Language: Английский

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