Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

Aslam, Bilal; Maqsoom, Ahsen; Cheema, Ali Hassan; Ullah, Fahim; Alharbi, Abdulaziz; Imran, Muhammad

doi:10.1109/access.2022.3221430

Cited by 23 publications

(6 citation statements)

References 77 publications

(112 reference statements)

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“…In another study, Alshboul et al [55] used a machine learning-based model to predict the shear strength of slender reinforced concrete beams. Aslam et al [56] used hybrid machine learning and data mining algorithms for water quality management. Bae et al [57] used an end-to-end deep super-resolution crack network (SrcNet) to improve computer vision-based automated crack detection in bridges to address the issues of motion blur and lack of pixel resolution.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

et al. 2023

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“…The following parameters are usually monitored by BWSSB (Table 1) [3,10]. The acceptable limits are also mentioned.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…GridSearchCV ultimately furnishes the optimal hyperparameter amalgamation, optimizing the model performance on the validation set. [3]…”

Section: Model Optimization and Evaluationmentioning

confidence: 99%

Machine Learning-based Water Potability Prediction: Model Evaluation, and Hyperparameter Optimization

Mondal,

Dubey

2024

Advancements in Communication and Systems

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This research aims to predict water potability, which is of utmost importance for community safety. A comprehensive analysis of a machine learning model is presented here, considering various quality parameters, to achieve this prediction. The model incorporates decision tree, KNN, Random Forest, SGD, SVM, logistic regression, and other algorithms. All steps of the study, including pre-processing, exploratory data analysis, feature scaling, model construction, assessment, and hyperparameter tuning, are thoroughly covered. Performance indicators like accuracy, confusion matrix, and classification report are used to evaluate the effectiveness of each model. Hyperparameter tweaking is implemented in decision trees, random forest algorithms, and K-nearest neighbors through grid search to optimize accuracy. The suggested model demonstrates its capability to forecast water portability accurately. It provides stakeholders with a systematic approach to model construction, evaluation, and optimization, thus ensuring water safety.

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“…These models are expected to offer a precise depiction of the mechanisms behind water quality deterioration [17]. To tackle this challenge, researchers have embraced the concept of modelling both surface and underground water quality utilizing soft computing tools, particularly machine learning models, due to their reputation for reliability and accuracy [18,19]. Nevertheless, these models have encountered difficulties in generalizing and effectively handling the intricate and highly nonlinear relationships among the various modelling parameters.…”

Section: Introductionmentioning

confidence: 99%

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Ramesh,

Vanitha

2024

Water

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The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain.

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Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach

Cited by 23 publications

References 77 publications

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Machine Learning-based Water Potability Prediction: Model Evaluation, and Hyperparameter Optimization

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

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