A framework of monitoring water pipeline techniques based on sensors technologies

Ayadi, Aya; Ghorbel, Oussama; BenSalah, M.S.; Abid, Mohamed

doi:10.1016/j.jksuci.2019.12.003

Cited by 30 publications

(22 citation statements)

References 33 publications

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“…In accordance with the reviewed literature, [30,69,75] the results show that the AdaBoost model is the most appropriate for predicting all parameters, with R ranged between 0.88 and 0.89, and that the random forest model is suitable for predicting only four parameters: TDS, PS, SAR, and ESP, with R ranged between 0.65 and 0.87. Added to that, as found by [22,76], this study identifies that The ANN and SVR models perform well in predicting three parameters (TDS, PS, SAR) and two parameters (PS, SAR), respectively, with most optimal value of generalization ability (GA) close to the unity.…”

Section: Discussionmentioning

confidence: 52%

Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia

Trabelsi

Ali

2022

Sustainability

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Over the last years, the global application of machine learning (ML) models in groundwater quality studies has proved to be a robust alternative tool to produce highly accurate results at a low cost. This research aims to evaluate the ability of machine learning (ML) models to predict the quality of groundwater for irrigation purposes in the downstream Medjerda river basin (DMB) in Tunisia. The random forest (RF), support vector regression (SVR), artificial neural networks (ANN), and adaptive boosting (AdaBoost) models were tested to predict the irrigation quality water parameters (IWQ): total dissolved solids (TDS), potential salinity (PS), sodium adsorption ratio (SAR), exchangeable sodium percentage (ESP), and magnesium adsorption ratio (MAR) through low-cost, in situ physicochemical parameters (T, pH, EC) as input variables. In view of this, seventy-two (72) representative groundwater samples have been collected and analysed for major cations and anions during pre-and post-monsoon seasons of 3 years (2019–2021) to compute IWQ parameters. The performance of the ML models was evaluated according to Pearson’s correlation coefficient (r), the root means square error (RMSE), and the relative bias (RBIAS). The model sensitivity analysis was evaluated to identify input parameters that considerably impact the model predictions using the one-factor-at-time (OFAT) method of the Monte Carlo (MC) approach. The results show that the AdaBoost model is the most appropriate model for predicting all parameters (r was ranged between 0.88 and 0.89), while the random forest model is suitable for predicting only four parameters: TDS, PS, SAR, and ESP (r was with 0.65 to 0.87). Added to that, this study found out that the ANN and SVR models perform well in predicting three parameters (TDS, PS, SAR) and two parameters (PS, SAR), respectively, with the most optimal value of generalization ability (GA) close to unity (between 1 and 0.98). Moreover, the results of the uncertainty analysis confirmed the prominent superiority and robustness of the ML models to produce excellent predictions with only a few physicochemical parameters as inputs. The developed ML models are relevant for predicting cost-effective irrigation water quality indices and can be applied as a DSS tool to improve water management in the Medjerda basin.

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Section: Discussionmentioning

confidence: 52%

Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia

Trabelsi

Ali

2022

Sustainability

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“…Kehilangan air merugikan PDAM selaku penyelenggara air minum, baik secara finansial maupun kualitas pelayanan. Dari sisi kualitas pelayanan, pipa distribusi air minum yang bocor mengakibatkan kerugian berupa berkurangnya kuantitas dan kualitas air yang dialirkan (Ayadi et al, 2020). Kehilangan air mengakibatkan tidak mampunya jaringan pipa mengalirkan air dengan baik karena terjadi penurunan tekanan akibat pipa bocor (Mustakim dan Pratama, 2018).…”

Section: Kehilangan Airunclassified

“…Total Waktu (T) yang dibutuhkan adalah waktu dari awareness time (A), location time (L) dan repair time (R) (Sánchez et al, 2020). Pendekatan yang umum dilakukan untuk mendeteksi kebocoran pipa seperti Visual Inspection (VI) dan Acoustic Detection (AD) memiliki kekurangan yaitu membutuhkan waktu, intervensi manusia dan keterbatasan dalam pelaksanaannya (Ayadi et al, 2020). Walaupun begitu, peneliti Sánchez et al, (2020) mengungkapkan cara deteksi kebocoran dengan metode AD tetap harus dilaksanakan, karena waktu deteksi lokasi kebocoran (L) sangat sulit untuk dikurangi.…”

Section: Mempercepat Waktu Tanggapunclassified

Peluang Dan Tantangan Pengendalian Kehilangan Air Berbasis Internet of Things (Iot) : Studi Pustaka

Asmara¹

2021

Jukung

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Permasalahan yang sering terjadi pada pipa distribusi air minum adalah kehilangan air. Kehilangan air mengurangi kualitas pelayanan dan pendapatan PDAM. Studi pustaka terhadap artikel ilmiah yang relevan menunjukkan adanya peluang penerapan pengendalian kehilangan air berbasis Internet of Things (IoT). Penelitian ini bertujuan untuk mengidentifikasi peluang dan tantangan penerapan IoT untuk pengendalian kehilangan air khususnya pada PDAM sebagai operator penyelenggara. IoT berpeluang mengendalikan kehilangan air melalui tiga cara yaitu manajemen tekanan, manajemen aset, dan mempercepat waktu tanggap saat terjadi kebocoran. Tantangan penerapan IoT pada PDAM di Indonesia berkaitan dengan kelembagaan dan pembiayaan PDAM, infrastruktur jaringan pendukung, kemanan data, serta ketidakakuratan dan kesalahan data. PDAM di Indonesia berpeluang menerapkan IoT namun hendaknya harus memperhatikan tantangan-tantangan yang telah dijabarkan pada penelitian ini. Kata kunci: Internet of Things (IoT), kehilangan air, PDAM. The problem that often occurs in water distribution pipes is water losses. The water losses reduce the service quality and the revenue of PDAM. The literature reviews of relevant scientific articles show that there are opportunities for implementing water losses control based on the Internet of Things (IoT). This study aims to identify the opportunities and the challenges of implementing IoT to control water losses at PDAM as an operator. The IoT has the opportunity to control water losses in three ways: pressure management; asset management; and accelerates the response time when a leak occurs. The challenges of implementing IoT at PDAM are related to the institutionalization and financing of PDAM; supporting network infrastructure; data security; and data inaccuracies and errors. PDAM in Indonesia has the opportunity to implement the IoT system, but they have to observe the possible challenges in this research. Keywords: Internet of Things (IoT), PDAM, water losses

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“…Figure 1 illustrates the classification of leak techniques into static and dynamic methods, followed by hardware-based and software-based methods. A detailed survey of these techniques along with a comparison can be found in [ 16 , 20 , 25 , 31 , 36 , 54 , 55 ]. In this study, our focus is on leak detection in WDN using WSN.…”

Section: State Of the Art Of Water Pipeline Monitoringmentioning

confidence: 99%

“…The scarcity of water thus requires that water losses due to leaks be minimized by accurately detecting and localizing leakages in real time, each time they occur. This has led to enormous research over the years in the field [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ], providing a wide range of methods for detecting and locating leaks in water pipelines.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

Nkemeni

Mieyeville

Tsafack

2020

Sensors

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Wireless Sensor Network (WSN) applications that favor more local computations and less communication can contribute to solving the problem of high power consumption and performance issues plaguing most centralized WSN applications. In this study, we present a fully distributed solution, where leaks are detected in a water distribution network via only local collaborations between a sensor node and its close neighbors, without the need for long-distance transmissions via several hops to a centralized fusion center. A complete approach that includes the design, simulation, and physical measurements, showing how distributed computing implemented via a distributed Kalman filter improves the accuracy of leak detection and the power consumption is presented. The results from the physical implementation show that distributed data fusion increases the accuracy of leak detection while preserving WSN lifetime.

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A framework of monitoring water pipeline techniques based on sensors technologies

Cited by 30 publications

References 33 publications

Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia

Exploring Machine Learning Models in Predicting Irrigation Groundwater Quality Indices for Effective Decision Making in Medjerda River Basin, Tunisia

Peluang Dan Tantangan Pengendalian Kehilangan Air Berbasis Internet of Things (Iot) : Studi Pustaka

A Distributed Computing Solution Based on Distributed Kalman Filter for Leak Detection in WSN-Based Water Pipeline Monitoring

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