Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models DOI Creative Commons
Soobin Kim, Eunhee Lee, Hyoun‐Tae Hwang

et al.

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

Robust machine learning algorithms for predicting coastal water quality index DOI Creative Commons
Md Galal Uddin, Stephen Nash, Mir Talas Mahammad Diganta

et al.

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 321, P. 115923 - 115923

Published: Aug. 19, 2022

Coastal water quality assessment is an essential task to keep "good quality" status for living organisms in coastal ecosystems. The Water index (WQI) a widely used tool assess but this technique has received much criticism due the model's reliability and inconsistence. present study recently developed improved WQI model calculating WQIs Cork Harbour. aim of research determine most reliable robust machine learning (ML) algorithm(s) anticipate at each monitoring point instead repeatedly employing SI weight values order reduce uncertainty. In study, we compared eight commonly algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra (ExT), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Naïve Bayes (GNB). For purposes developing prediction models, dataset was divided into two groups: training (70%) testing (30%), whereas models were validated using 10-fold cross-validation method. evaluate models' performance, RMSE, MSE, MAE, R2, PREI metrics study. tree-based DT (RMSE = 0.0, MSE MAE R2 1.0 PERI 0.0) ExT ensemble XGB +0.16 -0.17) RF 2.0, 3.80, 1.10, 0.98, +3.52 -25.38) outperformed other models. results performance indicate that DT, ExT, GXB could be effective, significantly uncertainty predicting WQIs. findings are also useful reducing optimizing WQM-WQI architecture values.

Language: Английский

Citations

140
The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management DOI Creative Commons
Hesam Kamyab, Tayebeh Khademi, Shreeshivadasan Chelliapan

et al.

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

Citations

124
Smart Water Resource Management Using Artificial Intelligence—A Review DOI Open Access
Siva Rama Krishnan Somayaji, M. K. Nallakaruppan, Rajeswari Chengoden

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(20), P. 13384 - 13384

Published: Oct. 17, 2022

Water management is one of the crucial topics discussed in most international forums. harvesting and recycling are major requirements to meet global upcoming demand water crisis, which prevalent. To achieve this, we need more emphasis on techniques that applied across various categories applications. Keeping mind population density index, there a dire implement intelligent mechanisms for effective distribution, conservation maintain quality standards purposes. The prescribed work discusses about few areas applications required efficient management. Those recent trends wastewater recycle, rainwater irrigation using Artificial Intelligence (AI) models. data acquired these purely unique also differs by type. Hence, use model or algorithm can be provide solutions all Deep Learning (DL) along with Internet things (IoT) framework facilitate designing smart system sustainable usage from natural resources. This surveys AI/DL IoT network case studies, sample statistical analysis develop an framework.

Language: Английский

Citations

115
Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam DOI Open Access
Đào Nguyên Khôi, Nguyen Trong Quan,

Do Quang Linh

et al.

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1552 - 1552

Published: May 12, 2022

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level existing surface water. This case study aims evaluate performance twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient histogram-based light extreme boosting), three decision tree-based (decision tree, extra trees, random forest), four ANN-based (multilayer perceptron, radial basis function, deep feed-forward neural network, convolutional network), in estimating quality La Buong River Vietnam. Water data at monitoring stations alongside for period 2010–2017 were utilized calculate index (WQI). Prediction ML models was evaluated by using two efficiency statistics (i.e., R2 RMSE). The results indicated that all have good predicting WQI but boosting (XGBoost) has best with highest accuracy (R2 = 0.989 RMSE 0.107). findings strengthen argument especially XGBoost, may be employed prediction a high accuracy, which will further improve management.

Language: Английский

Citations

102
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions DOI Creative Commons
Kumar Puran Tripathy, Ashok K. Mishra

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 628, P. 130458 - 130458

Published: Nov. 15, 2023

Language: Английский

Citations

95
Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms DOI
Swapan Talukdar,

Shahfahad,

Shakeel Ahmed

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

Citations

62
Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model DOI
Usman Mohseni,

Chaitanya B. Pande,

Subodh Chandra Pal

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141393 - 141393

Published: Feb. 5, 2024

Language: Английский

Citations

37
Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model DOI Creative Commons
Md Galal Uddin, Azizur Rahman, Firouzeh Taghikhah

et al.

Water Research, Journal Year: 2024, Volume and Issue: 255, P. 121499 - 121499

Published: March 20, 2024

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that model produced inconsistent results due to data outliers, which significantly impact reliability accuracy. The present study was carried out assess of outliers on recently developed Irish Water Quality Index (IEWQI) model, relies techniques. To author's best knowledge, no systematic framework for evaluating influence such models. For purposes assessing outlier (WQ) this first initiative research introduce comprehensive approach combines with advanced statistical proposed implemented Cork Harbour, Ireland, evaluate IEWQI model's sensitivity input indicators quality. In order detect outlier, utilized two widely used ML techniques, including Isolation Forest (IF) Kernel Density Estimation (KDE) within dataset, predicting WQ without these outliers. validating results, five commonly measures. performance metric (R2) indicates improved slightly (R2 increased from 0.92 0.95) after removing input. But scores were statistically differences among actual values, predictions 95% confidence interval at p < 0.05. uncertainty also contributed <1% final assessment using both datasets (with outliers). addition, all measures indicated techniques provided reliable can be detecting their impacts model. findings reveal although had architecture, they moderate rating schemes' This finding could improve accuracy as well helpful mitigating eclipsing problem. provide evidence how influenced reliability, particularly since confirmed effective accurately despite presence It occur spatio-temporal variability inherent indicators. However, assesses underscores important areas future investigation. These include expanding temporal analysis multi-year data, examining spatial patterns, detection methods. Moreover, it is essential explore real-world revised categories, involve stakeholders management, fine-tune parameters. Analysing across varying resolutions incorporating additional environmental enhance assessment. Consequently, offers valuable insights strengthen robustness provides avenues enhancing its utility broader applications. successfully adopted affect current Harbour only single year data. should tested various domains response terms resolution domain. Nevertheless, recommended conducted adjust or revise schemes investigate practical effects updated categories. potential recommendations adaptability reveals effectiveness applicability more general scenarios.

Language: Английский

Citations

36
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
Applications of deep learning in water quality management: A state-of-the-art review DOI

Kok Poh Wai,

Min Yan Chia,

Chai Hoon Koo

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 613, P. 128332 - 128332

Published: Aug. 23, 2022

Language: Английский

Citations

67