An integrated approach of GIS and geoelectrical techniques for wastewater leakage investigations: Active constraint balancing and genetic algorithms application

Attwa, Mohamed; Zamzam, Sara

doi:10.1016/j.jappgeo.2020.103992

Cited by 22 publications

(9 citation statements)

References 27 publications

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“…A recent review of GA with a focus on the crossover and mutation rate choice was made by [ 69 ]. Recently, in the work by [ 70 ], GA was used to optimize a novel real-time control system for mitigation of sewer flooding and in [ 71 ] GA is implemented in an algorithmic procedure to investigate the wastewater seepage appearance in a semiarid urban environment.…”

Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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This paper presents and explores a novel methodology for solving the problem of a water distribution network contamination event, which includes determining the exact source of contamination, the contamination start and end times and the injected contaminant concentration. The methodology is based on coupling a machine learning algorithm for predicting the most probable contamination sources in a water distribution network with an optimization algorithm for determining the values of contamination start time, end time and injected contaminant concentration for each predicted node separately. Two slightly different algorithmic frameworks were constructed which are based on the mentioned methodology. Both algorithmic frameworks utilize the Random Forest algorithm for classification of top source contamination node candidates, with one of the frameworks directly using the stochastic fireworks optimization algorithm to determine the contamination start time, end time and injected contaminant concentration for each predicted node separately. The second framework uses the Random Forest algorithm for an additional regression prediction of each top node’s start time, end time and contaminant concentration and is then coupled with the deterministic global search optimization algorithm MADS. Both a small sized (92 potential sources) network with perfect sensor measurements and a medium sized (865 potential sources) benchmark network with fuzzy sensor measurements were used to explore the proposed frameworks. Both algorithmic frameworks perform well and show robustness in determining the true source node, start and end times and contaminant concentration, with the second framework being extremely efficient on the fuzzy sensor measurement benchmark network.

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Section: Materials and Methodsmentioning

confidence: 99%

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Grbčić

Kranjčević

Družeta

2021

Sensors

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“…Analytical networks are widely used in modeling analysis and most frequently used in GIS-MCDA (Musakwa et al, 2017;Kausika et al, 2017;Achu et al, 2020;Shao et al, 2020). The ANP model is more efficient because it identifies possible dependencies among the selected criteria (Saaty, 1980).…”

Section: Suitable Locationsmentioning

confidence: 99%

A novel framework for analyzing urban water network events and determining the optimal location of urban drinking water relief posts

Garajeh

2023

Preprint

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Urban water is known as critical sector of urban environments which significantly impacts the life quality and wellbeing of reinstates. In the context of developing sustainable urban drinking system it is critical to analysis network events and develop sufficient systems of water supply. In following this objective, the current study aims to propose an efficient approach for Geospatial based urban water network events analyze and determine the optimal location of urban drinking water relief posts in Zanjan. For this goal, first, we prepared and preprocessed various predisposing variables such as water storage sources, pump stations, traffic density, population density, road network, land use, subscribers, areas with higher water pressure, and accident-prone areas for analyzing the urban water network events and determining the optimal location of urban drinking water relief posts. We then applied an integrated approach of analytical network process (ANP) and deep learning convolutional neural network (DL-CNN) data-driven methods to locate the optimal place of urban drinking water relief posts. Finally, intersection over union and accuracy assessment were employed to evaluate the performance of the results. Our findings show that the DL-CNN performed well with the accuracy of 0.942 than the ANP (0.895) for determining the optimal location of urban drinking water relief posts. The results indicate that the most suitable places for building a relief post are in the center of the city and its surrounding areas might not suitable due to barren lands and sparse population. The results of the study also reveal that areas 5 and 3 are at high risk from the number of urban water network incidents perspective, which require the construction of urban water relief stations.

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“…Due to its up scalability and simplicity, the DCR method is widely used for environmental studies e.g., [30]. Accordingly, the majority of DCR measurements in the form of 2D-ERTs and soundings were carried out in the study dumpsite over two years.…”

Section: Geophysical and Borehole Datamentioning

confidence: 99%

Qualitative and Quantitative Characterization of Municipal Waste in Uncontrolled Dumpsites and Landfills Using Integrated Remote Sensing, Geological and Geophysical Data: A Case Study

et al. 2022

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The conducted research offers an environmental assessment of municipal waste (MW) using remote sensing (RS), geological, and geophysical datasets. As a test site, the present study aims to characterize one of the largest uncontrolled dumpsites in Egypt. RS data analysis indicates that high temperature values are concentrated at the MW and landfill site allocations as a result of the decomposition process, leading to fire risks. Moreover, the geological and structural data draw attention to the structural-controlled old topography role on MW distributions. Consequently, the dumpsite MW has larger surface quantities near the downthrows of the mapped faults. For MW characterization, geophysical data are acquired to produce2D/3D resistivity models. Because MW has the ability to become soft clay, the municipal organic waste and landfill leachate resistivities are lower than that of municipal solid waste and sandy soils. The geophysical inversion results indicate that the maximum thickness of MW calibrating with the drilled borehole data is 60 m. Furthermore, the estimated MW dumpsite volume is about 42.32 MCM. Accordingly, the MW can be characterized in an accurate qualitative and quantitative manner. Our findings, therefore, help the efforts of uncontrolled dumpsite development and thus contribute to sustainability plans.

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An integrated approach of GIS and geoelectrical techniques for wastewater leakage investigations: Active constraint balancing and genetic algorithms application

Cited by 22 publications

References 27 publications

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection

A novel framework for analyzing urban water network events and determining the optimal location of urban drinking water relief posts

Qualitative and Quantitative Characterization of Municipal Waste in Uncontrolled Dumpsites and Landfills Using Integrated Remote Sensing, Geological and Geophysical Data: A Case Study

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