Deep Learning-Based Prediction of Seawater Intrusion Using recurrent architectures: application on Kalymnos Island DOI Creative Commons
George Kopsiaftis, Eftychios Protopapadakis, Maria Kaselimi

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

Abstract This study explores the application of deep learning models (DL) for prediction seawater intrusion in coastal aquifers, under time-varying recharge and pumping conditions, Kalymnos Island, Greece. The models, based on recurrent architectures, i.e. RNN, LSTM, GRU, are trained to simulate temporal dynamics front. For creation dataset, a detailed 3D variable density model was developed, capturing transient behavior over 50-year period, using monthly variations. results demonstrate that bidirectional exhibit superior performance complex dependencies, achieving lower errors compared unidirectional models. underscores utility DL as efficient surrogates computationally intensive hydrodynamic simulations, presenting viable approach sustainable aquifer management.

Language: Английский

Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning algorithms DOI
Khabat Khosravi, Aitazaz A. Farooque, Masoud Karbasi

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

Language: Английский

Citations

4
Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends DOI
Asish Saha, Subodh Chandra Pal

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 632, P. 130907 - 130907

Published: Feb. 16, 2024

Language: Английский

Citations

16
Performance Evaluation and Triangle Diagram of Deep Learning Models for Embedment Depth Prediction in Cantilever Sheet Piles DOI Open Access
Thalappil Pradeep,

Divesh Ranjan Kumar,

Nitish Kumar

et al.

Engineered Science, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Sheet piles are essential for maintaining the stability and retention of soil in various applications, including railway highway embankments, offshore structures, post-excavation sites, slope stabilization projects.The required depth sheet is contingent upon factors such as characteristics, groundwater conditions, employed construction method.This study focused on predicting embedment cantilever pile walls cohesive with a cohesionless backfill.Artificial intelligence (AI) techniques, specifically deep neural networks (DNNs), recurrent (RNNs), long short-term memory (LSTM) networks, bidirectional (Bi-LSTM) applied this purpose.Performance evaluation conducted through rank analysis, performance parameter determination, comparison actual versus predicted curves, accompanied by an error plot.A triangle diagram introduced graphical representation to assess different datasets or models.External validation was evaluate generalizability

Language: Английский

Citations

6
Using the TSA-LSTM two-stage model to predict cancer incidence and mortality DOI Creative Commons
Rabnawaz Khan, Jie Wang

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0317148 - e0317148

Published: Feb. 20, 2025

Cancer, the second-leading cause of mortality, kills 16% people worldwide. Unhealthy lifestyles, smoking, alcohol abuse, obesity, and a lack exercise have been linked to cancer incidence mortality. However, it is hard. Cancer lifestyle correlation analysis mortality prediction in next several years are used guide people's healthy lives target medical financial resources. Two key research areas this paper Data preprocessing sample expansion design Using experimental comparison, study chooses best cubic spline interpolation technology on original data from 32 entry points 420 converts annual into monthly solve problem insufficient prediction. Factor possible because sources indicate changing factors. TSA-LSTM Two-stage attention popular tool with advanced visualization functions, Tableau, simplifies paper's study. Tableau's testing findings cannot analyze predict time series data. LSTM utilized by optimization model. By commencing input feature attention, model technique guarantees that encoder converges subset sequence features during output features. As result, model's natural learning trend quality enhanced. The second step, performance maintains We can choose network improve forecasts based real-time performance. Validating source factor using Most cancers overlapping risk factors, excessive drinking, exercise, obesity breast, colorectal, colon cancer. A poor directly promotes lung, laryngeal, oral cancers, according visual tests. expected climb 18-21% between 2020 2025, 2021. Long-term projection accuracy 98.96 percent, smoking may be main causes.

Language: Английский

Citations

0
MVIE-LSTM: a deep learning-based method for water quality assessment using monthly river data DOI

Sha Xiong,

Junjie Cui, Feifei Hou

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Language: Английский

Citations

0
Reliable Water Quality Prediction Using Bayesian Multi-Scale Convolutional Attention Network DOI Open Access
Xiaolin Guo

Journal of Geoscience and Environment Protection, Journal Year: 2025, Volume and Issue: 13(03), P. 347 - 363

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Mamba-based method for multi-feature water quality prediction fusing dual denoising and attention enhancement DOI

Xianbao Tan,

Yulong Bai, Xin Yue

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133424 - 133424

Published: April 1, 2025

Language: Английский

Citations

0
A generalized machine learning approach for cost-effective monitoring of irrigation suitability: A demonstration case in El Fahs aquifer (Tunisia) DOI Creative Commons
Constantinos F. Panagiotou, Charalampos Konstantinou, Anis Chekirbane

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 27, P. 101324 - 101324

Published: Aug. 27, 2024

Language: Английский

Citations

2
Groundwater pollution equation: Lie’s symmetry analysis and numerical consideration DOI Creative Commons
A. F. Aljohani, Abdulhamed Alsisi, Saad Althobaiti

et al.

Partial Differential Equations in Applied Mathematics, Journal Year: 2024, Volume and Issue: 11, P. 100861 - 100861

Published: Aug. 10, 2024

The current study modeled groundwater pollution through the utilization of advection–diffusion equation - a versatile differential that is capable modeling variety real-life processes. Indeed, various methods solutions were then proposed to examine governing model after being transformed, starting with Lie's symmetry, semi-analytical, and numerical methods, including explicit implicit finite difference method element method. Further, demonstrated on some test models; featuring forced unforced scenarios forcing function. Analytically, symmetry failed unswervingly reveal required solution problem; however, imposition certain restrictions, generalized closed-form for was acquired. This fact indeed triggered quest deployment more methods. Thus, semi-analytically, adopted decomposition swiftly gave resultant solutions. Numerically, efficiency sought assessed using L2−norm CPU time, upon which schemes found win race. All-in-all, beseeched semi-analytical highly recommended such investigation; at same time advocating effectiveness advection–diffusion-related equations.

Language: Английский

Citations

1
A Multivariable Probability Density-Based Auto-Reconstruction Bi-LSTM Soft Sensor for Predicting Effluent BOD in Wastewater Treatment Plants DOI Creative Commons
Wenting Li,

Yonggang Li,

Dong Li

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7508 - 7508

Published: Nov. 25, 2024

The precise detection of effluent biological oxygen demand (BOD) is crucial for the stable operation wastewater treatment plants (WWTPs). However, existing methods struggle to meet evolving drainage standards and management requirements. To address this issue, paper proposed a multivariable probability density-based auto-reconstruction bidirectional long short-term memory (MPDAR-Bi-LSTM) soft sensor predicting BOD, enhancing prediction accuracy efficiency. Firstly, selection appropriate auxiliary variables soft-sensor modeling determined through calculation k-nearest-neighbor mutual information (KNN-MI) values between global process BOD. Subsequently, considering existence strong interactions among different reaction tanks, Bi-LSTM neural network model constructed with historical data. Then, multivariate (MPDAR) strategy developed adaptive updating model, thereby its robustness. Finally, effectiveness demonstrated experiments using dataset from Benchmark Simulation Model No.1 (BSM1). experimental results indicate that not only outperforms some traditional models in terms performance but also excels avoiding ineffective reconstructions scenarios involving complex dynamic conditions.

Language: Английский

Citations

1