| Project Id
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BITSRMIT024B001265
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| Project Detail
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| Project Title
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Climate-Resilient Smart Groundwater Recharge Solutions for Arid and Semi-Arid Areas
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| Senior Supervision Team (BITS)
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| Supervisor name and Title
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Dr. Rallapalli Srinivas
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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|
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Email ID
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r.srinivas@pilani.bits-pilani.ac.in
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| URL for more info
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https://bits-pilani.irins.org/profile/228172
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
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4
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| b) Have you supervised an offshore candidate before?
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NO
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| If no, what support structures do you have in place?
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|
| If yes, please elaborate
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| Senior Supervision Team (RMIT)
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| Supervisor name and Title
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Prof. Guomin (Kevin) Zhang
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School or Department (or company, if applicable)
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STEM
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Email ID
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kevin.zhang@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/contact/staff-contacts/academic-staff/z/zhang-kevin
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| a) Are you currently supervising a BITS or RMIT HDR student?
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YES
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| Please comment how many you are supervising
|
8
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| b) Have you supervised an offshore candidate before?
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YES
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| If no, what support structures do you have in place?
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|
| If yes, please elaborate
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I supervise students in the joint PHD program from Sri Lanka Universities. I meet the students on a fortnightly basis via teams.
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Prof. Ajit Pratap Singh
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School or Department (or company, if applicable)
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BITS PILANI, PILANI CAMPUS
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| Phone Number (Optional)
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9664031566
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Email ID
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aps@pilani.bits-pilani.ac.in
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| URL for more info
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https://www.bits-pilani.ac.in/pilani/ajit-pratap-singh/
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| Other Supervisors (BITS)
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| Supervisor name and Title
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Muhammed Bhuiyan Senior Lecturer
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School or Department (or company, if applicable)
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STEM
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| Phone Number (Optional)
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399259014
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Email ID
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muhammed.bhuiyan@rmit.edu.au
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| URL for more info
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https://www.rmit.edu.au/contact/staff-contacts/academic-staff/b/bhuiyan-dr-muhammed
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| Field of Research (For Codes)
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| 370201 | Climate Change Processes | 25.00 |
| 370703 | Groundwater Hydrology | 50.00 |
| 461199 | Machine learning not elsewhere classified | 25.00 |
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| Project Description
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|
Aims: Groundwater levels over much of India and some regions of Australia are declining because usage exceeds seasonal monsoonal recharge. This proposal aims to increase groundwater recharge through groundwater recharge potential zones (GRPZ) using remote sensing and geospatial technology to target areas of more efficient infiltration for installing rainwater harvesting (RWH) infrastructure. Machine learning algorithms will be used to map GRPZ. Groundwater level changes will be compared with AI/GCM climate projections to gauge the effectiveness of increased recharge in mitigating climate change impacts. Water samples from rainwater and groundwater will be analyzed for quality. Groundwater users in the target areas will be surveyed to design an incentivization and capacity-building program to encourage the adoption of improved infiltration technology across both countries.
Objectives:
1. increasing RWH efficiency through i. better siting of infrastructures using remote sensing and geospatial technology, ii. creating an AI system to GRPZ.
2. to evaluate RWH groundwater resiliency and how it can offset regional climate change,
3. developing an incentive plan as well as a capacity building and training plan for groundwater stakeholders to overcome impediments to widespread adoption of the technology.
Methodology
Phase I: Improving efficiency of RWH through targeting areas that allow maximum infiltration. Activities include:
1. Rural area will be chosen to identify the parts with porous soil
2. GRPZ mapping using machine learning and remotely sensed and geospatial datasets.
3. Monitoring bores with water level loggers will be installed to record the impact of more efficient RWH on the local groundwater levels.
4. RWH sites will be located to minimise the potential for contamination by surface pollutants.
Phase II: Developing AI framework to analyze the relationship between climate change and groundwater levels at a regional level. Steps include:
1. Integration of Data Sources in a unified platform
2. Machine Learning/Deep Learning (EfficeintNet, ResNet, MobileNet, Vision Transformer, etc.) Pipeline
Phase III: Widespread adoption of the proposed RWH technology. Steps include:
1. Identify and map key stakeholders
2. Statistical and econometric analysis
3. Develop a Computable General Equilibrium (CGE) model
4. Engage with local communities through surveys, interviews, and focus group discussions.
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| Project Deliverable/Outcomes
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1. Rigorous dataset documenting the impact of more efficient RWH on groundwater resources in arid and semi-arid areas [Deliverable: New/Upgraded System].
2. AI framework for groundwater resiliency estimation with respect to the changing climatic condition [Deliverable: New/Upgraded System, New/Upgraded Process, Software].
3. AI system to estimate the groundwater recharge potential zones [Deliverable: New/Upgraded System, Software].
4. User interface and manual to incentivize stakeholder engagement and promote community-based RWH initiatives through policy recommendations, training and education, community engagement [Deliverable: New/Upgraded System, New/Upgraded Process, Software].
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| Research Impact Themes
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| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| SOCIAL AND ECONOMIC CHALLENGES IN ENERGY, WATER, FOOD, FINANCIAL MARKETS AND INFRASTRUCTURE AND PREVENTING ENVIRONMENTAL DEGRADATION |
| SUSTAINABLE DEVELOPMENT AND ENVIRONMENT
| SUSTAINABLE TECHNOLOGIES |
| ENHANCED LIVABILITY AND URBAN FUTURES | URBAN ENVIRONMENTS AND SMART CITIES, WATER STEWARDSHIP AND EFFECTIVE WATER USE |
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| Which RMIT Sustainable Development Goal (SDG) does your project align to
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CLEAN WATER AND SANITATION
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| Which RMIT Enabling Impact Platform (EIP) does your project align to
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SUSTAINABLE TECHNOLOGIES AND SYSTEMS PLATFORM
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| Which RMIT Program code will this project sit under?
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DR218 (CIVIL)
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| Student Capabilities and Qualifications
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Background with environmental/water resources engineering; experience of numerical simulation modeling using MATLAB
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Software/Coding Skills: ML, MODFLOW
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MSc. or Mtech or M.E.
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| Preferred discipline of Student
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| Artificial Intelligence, Deep Learning, Information Extraction & Knowledge Extraction, Machine Learning, Natural Language Processing |
| Civil Engineering, Structural Engineering |
| Environmental Engineering |
| Natural Resources, Water Resources |
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