Water Losses Management in Urban Water Distribution Systems

Karadirek, I. Ethem; Aydın, Mehmet Emin

doi:10.1007/978-3-030-95288-4_6

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…Supply diversi cation, on the other hand, focuses on offering alternative water sources, such as surface water, groundwater, water roof harvesting, and recycling (Musango et al,2017). In the simplest term, water-sensitive intervention can be linked with nding a reliable, accessible, quality water supply, xing huge leaks, controlling and enforcing restrictions on unauthorized connections, and looking for other alternative water sources (Karadirek &Aydin, 2022 andVasileios et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Tracing and Tracking Residential Water Consumption in Adama City, Ethiopia: Coupling Geospatial and Socioeconomic Analysis for Water-Sensitive Intervention Decision-Making Support

Abraha

Tibebu

Ephrem

2023

Preprint

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To develop a water-conscious strategy, it is critical to trace and track water from its source to the end users, understand water conservation behaviors and identify the factors that influence water usage. However, in developing countries, comprehensive studies on water consumption are still scarce, and those that are already available are difficult to generalize and do not provide a full picture for decision-making. Hence, the main objective of the study is to trace and track water consumption from its source to end users to generate water consumption information for water-sensitive intervention decision-making. In terms of methodology, both secondary and primary data were gathered using a household survey and a Geospatial technique. The current analysis found that internalization and diverse water harvesting methods are not taken into account and that there is just "one source option" for the supply. The result asserted that the average water consumption per capita per day is 73 L/person/day. This falls short of both the national standard (80 l/person/day) and the WHO standard, the water delivery network only covers 45% of the city master plan, 38% of the demand is unmet and 40% of the city area coverage receiving water no more frequently than once per week. This shows that the city is experiencing a physical and economic water scarcity. The findings also demonstrated that variables like family size, housing quality, closeness to the city center, and reliability have a big impact on water consumption. Based on the findings, it is possible to infer that water crisis will continue at an accelerating rate so long as the ‘business as usual’ water consumption practice remains unchanged.

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Section: Introductionmentioning

confidence: 99%

Tracing and Tracking Residential Water Consumption in Adama City, Ethiopia: Coupling Geospatial and Socioeconomic Analysis for Water-Sensitive Intervention Decision-Making Support

Abraha

Tibebu

Ephrem

2023

Preprint

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A Case Study on Controlling Real Losses in Large-Scale Water Distribution Systems

Akdeniz,

Cakmak,

Karadirek

2024

Water and Wastewater Management

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Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

Tesfay Abraha,

Assefa Woldeamanuel,

Gebremariam Beyene

2024

npj Clean Water

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To develop a water conscious strategy, it is critical to track and trace water from its source to the end users, understand water conservation behaviors, and identify the factors that influence water consumption. However, in developing nations, little research has been done to provide a quantitative picture of how water is consumed and transformed in urban households, as well as the water sensitive interventions needed to improve access to clean water. Hence, the main objective of the study was to determine the most significant residential water consumption variables and to predict residential water consumption in a way that can generate water consumption information for water sensitive intervention decision making using the case study of Adama city in Ethiopia. A combination of top down and bottom up data collection techniques were employed as the data collection instrument. Machine learning was integrated with spatial and socioeconomic analytic techniques to estimate daily household water consumption and identify the factors that significantly influence household water consumption. The results show that there is only “one source option” for the city’s clean water supply and that different water harvesting methods are not likely to be developed. The average daily water consumption per person is 69 liters which falls below the national standard of 80 liters allocated per person per day. The result reveals that the water distribution network covers only 45% of the city master plan. About 38% of the water demand is unmet and 30% of households only receive water once every three days or fewer. This shows that the city is experiencing physical and economic water scarcity. The results demonstrated that family size, housing quality, income, number of rooms, legal status of the parcel, supply reliability, climate, and topographical features are the most important factors in predicting residential water consumption. This study further demonstrates how well supervised machine learning models, such as the Random Forest Regression algorithm, can predict the household’s daily water consumption. The findings also showed that there is a need for significant improvements in water saving habits of the households. Another conclusion that can be drawn is that as long as the city’s business as usual water consumption practice doesn’t change, the water supply problem will worsen over time.

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Water Losses Management in Urban Water Distribution Systems

Cited by 3 publications

References 47 publications

Tracing and Tracking Residential Water Consumption in Adama City, Ethiopia: Coupling Geospatial and Socioeconomic Analysis for Water-Sensitive Intervention Decision-Making Support

Tracing and Tracking Residential Water Consumption in Adama City, Ethiopia: Coupling Geospatial and Socioeconomic Analysis for Water-Sensitive Intervention Decision-Making Support

A Case Study on Controlling Real Losses in Large-Scale Water Distribution Systems

Tracking and tracing water consumption for informed water sensitive intervention through machine learning approach

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