{"id":6643,"date":"2022-03-09T11:48:38","date_gmt":"2022-03-09T09:48:38","guid":{"rendered":"https:\/\/www.adssc.org\/en\/?p=6643"},"modified":"2025-09-29T11:06:42","modified_gmt":"2025-09-29T09:06:42","slug":"cohen-meirav-phd-hydrology","status":"publish","type":"post","link":"https:\/\/www.adssc.org\/en\/cohen-meirav-phd-hydrology\/","title":{"rendered":"Cohen Meirav Ph.D


Hydrology<\/h4>"},"content":{"rendered":"\t\t
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Meirav Cohen Ph.D
\"\"<\/h1>

Research Area:
Hydrology and Water Quality<\/strong><\/h4>

\"\"meirav@adssc.org<\/a>
\"\"972-055-8912071
\"\"Google Scholar<\/a>
\"\"ResearchGate<\/a><\/p>

Research Interests <\/strong><\/h3>
  • Contaminants transport in the saturated and unsaturated zones<\/li>
  • Contaminant degradation in wastewater systems and subsurface environments<\/li>
  • Systems optimization (wastewater and others)<\/li>
  • Prediction of groundwater quality and quantity<\/li>
  • Basin scale water flows<\/li>
  • Modelling vegetation cover<\/li><\/ul>

    \u00a0<\/h3>

    Current Projects<\/strong><\/h3>

    Modelling and Optimization of Decentralized (Off-Grid) Wastewater Systems Using Machine Learning Models<\/strong>
    Principal Investigator<\/em>
    Collaboration: Prof. Amit Gross, Dr. Michael Fire, Prof. Roy Bernstein, and Dr. Elad Levintal (Ben-Gurion University).
    PhD Student: Tom Norman
    Funded by: The Ministry of Energy and Infrastructure<\/p>

    In remote and isolated areas, such as lone farms, connecting to the central wastewater treatment system is often economically, technically, and environmentally unfeasible. Localized wastewater treatment systems are required, which can be adapted to specific site needs and provide treated water for local irrigation. However, the primary drawback of such systems is the lack of continuous monitoring to detect malfunctions and infrequent water quality checks. To ensure high water quality and prevent malfunctions, approved systems are equipped with energy-intensive tools, often unnecessarily.
    This study explores ways to improve, predict water quality, and detect malfunctions in localized systems using inexpensive sensors and machine learning models. The concept involves monitoring simple parameters in an operational system and using them to predict water quality, a parameter that is complex to measure. The model and continuous monitoring aim to improve system efficiency, alert against failures, and adapt systems to various environments.<\/p>

    \"\"<\/p>

    An overview of the wastewater treatment system at Karmey Avdat farm and the sensors installed as part of the project<\/h6>

    \u00a0<\/p>

    Evaluating a Method for Restoring Springs in the Arava through the Creation of Subsurface Hydrological Barriers<\/strong>
    Principal Investigator<\/em>
    Collaboration: Dr. Avshalom Babad and Dr. Roy Galili
    Research Assistant and Postdoctoral Fellows: Naomi Barda and Dr. Ben Zang
    Funded by: The Open Spaces Fund<\/p>

    Historically, many springs emerged along the western margins of the Arava, a region known as the “Spring Route.” Out of about 30 springs active in the 1950s, only 13 currently remain active, while the rest have dried up. The reasons for this are over-pumping from aquifers feeding the springs and consecutive dry years in the Arava. Spring drying is evident throughout the region, leading to gradual drying of most natural vegetation around the springs. To conserve water flow and natural vegetation in springs, human intervention appears necessary. This research examines the application of a subsurface hydrological barrier to capture upstream floodwaters, creating a localized groundwater lens that can restore local vegetation, particularly the dwindling date palm population. The study focuses on the Ein Shachak spring, with its lone surviving date palm as a case study for the Arava springs.
    The research involves characterizing the subsurface and drainage basin and development of a hydrological model to simulate the hydrological barrier. This model, along with a small barrier implemented in the field, will allow conclusions regarding the feasibility of restoring the Ein Shachak spring and other springs.<\/p>

    \"\"<\/p>

    Ein Shachak spring before its drying and after in present times, along with an overview of the model developed as part of the project.<\/h6>

    \u00a0<\/p>

    Large Scale Groundwater Salinization Prediction and Understanding Using Machine Learning Models<\/strong>
    Principal Investigator<\/em>
    Indo-Israeli Collaboration: Dr. Advai Mitra
    Postdoctoral Fellow: Dr. Ariel Meroz
    Funded by: Ministry of Science, Indo-Israel Research Fund<\/p>

    Groundwater is the largest freshwater reservoir on Earth, containing nearly 70 times more water than surface sources and providing approximately 33% of global drinking water. However, groundwater resources face overexploitation and quality degradation, with one of the primary threats being salinization. This research aims to understand salinization processes and assess potential impacts on aquifers of groundwater management, climate change, and population growth. A comprehensive dataset of factors potentially influencing groundwater has been compiled. This dataset serves as the basis for a broad, multidisciplinary database offering a holistic view of groundwater conditions and potential influencing factors. Machine learning models are employed to the dataset to predict salinity and identify dominant salinization processes across different spatial scales and a deep learning model is developed for spatial and spatiotemporal analyses. The continued development and application of these tools for future predictions of groundwater properties will enable the prevention of future groundwater deterioration and smarter groundwater resource management in Israel and globally.<\/p>

    \"\"<\/p>

    Grounwater boreholes and basisns modelled in this project<\/h6>

    \u00a0<\/p>

    Publications:<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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