Challenge 1: Leveraging emerging digital technologies to strengthen water security with Managed Aquifer Recharge
MARSite: Neural Network-Based Evaluator for Potential Managed Aquifer Recharge (MAR) Sites
Finding the ideal geographical MAR site is difficult, costly, and lengthy work even for large teams. We wanted to facilitate this process and help them find the best candidate locations for building MAR sites, so we decided to create MARS (short for MARSites): the MAR site finder.
Before we dive into the exciting innovative and engineering part, let’s catch up on the basics of water and aquifers.
The Significance of Groundwater Security
For approximately 2.5 billion people worldwide [1], groundwater is their sole source of daily water needs, making it indispensable to global water security. Groundwater provides drinking water to nearly half of the global population and supports 43% of all irrigation. Sustainable groundwater management is critical because the Earth’s population is expected to reach 10.4 billion by 2100 [2].
Source: WORLD BANK [3]
Rapid Groundwater Depletion
The depletion of significant aquifers worldwide is a growing concern, leading to decreased stream flow, drying of springs and wetlands, loss of vegetation, declining water levels in wells, and even land subsidence. Additionally, pollution from human activities, such as chemical leaks and waste disposal, threatens groundwater quality, posing risks to human and ecological health.
A recent comprehensive study published in Nature reveals that groundwater is being rapidly depleted across numerous global aquifers, as evidenced by water level changes measured in 170,000 wells. The analysis indicates that this depletion is not only widespread but also accelerating, with some areas experiencing annual declines of 20 inches or more [4].
MAR 101: Why Managed Aquifer Recharge is a Game-Changer
Managed Aquifer Recharge (MAR) is a cost-effective and efficient method for managing groundwater. It involves intentionally replenishing aquifers using natural methods (like infiltration basins) or engineered solutions (such as injection wells). MAR addresses groundwater depletion by storing excess water during wet periods, which can then be used in dry times, reducing evaporation losses common in surface storage [5].
Key benefits of MAR include:
- Cost-Effectiveness: Lower implementation costs compared to other methods.
- Reduced Evaporation Losses: More efficient in conserving water than surface reservoirs.
- Increased Water Supply: Enhances water availability for agricultural, industrial, and domestic use.
- Groundwater Stabilization: Mitigates issues like declining water tables and land subsidence.
- Versatility: Can utilize various water sources, including stormwater and treated wastewater.
But…
Everything so far sounds great. However, there are several pressing challenges when it comes to creating a suitable MAR site.
- Cost: Over 25% + of capital alone is spent on investigating the suitability of the MAR sites.
- HR: Insufficient skilled Human Resources to produce a sound technical assessment
- Time: It takes several months to wrap up the investigation depending on the location circumstances
How we address this issue
We want to address this problem by automating the initial investigation to determine the suitability of the MAR sites. Through our solution:
- The cost will be significantly reduced so people can get started and move quickly
- Overcome the difficulty of finding skilled people
- Save time and resources
Our solution comprises two main parts: The multi-criteria Decision analysis model and the Climate prediction model. Let’s look more in-depth at how we combine the best of both worlds.
Multi-Criteria Decision Analysis
The solution will be primarily focused on an MCDA (Multi-Criteria Decision Analysis) technique ANP (The Analytic Network Process).
Initially proposed as a generalization of the Analytic Hierarchy Process (AHP), another Multi-Criteria Decision Analysis (MCDA) technique, the Analytic Network Process (ANP) is a systematic method used to break down a problem into its constituent components [7]. The ANP follows a holistic approach where all influencing criteria, along with their potential dependencies, are organized in a network structure (Fig. a).
Utilizing the ANP can be broadly divided into two steps: the network creation and network analysis.
The network creation: At this stage, the primary goal is to structure the initial problem to identify the overall goal, sub-goals, criteria, sub-criteria, and alternatives, if any. These elements are then grouped into clusters based on their common themes, creating clusters of related elements. Finally, any dependencies between different clusters, as well as within the elements of the same cluster, are identified and represented with arrows. Arrows with the same source and destination, known as loops, indicate internal dependencies within a cluster.
The network analysis: At this stage, the network is analyzed both analytically and empirically. Particularly, the Markov-Chain process is used on a stochastic supermatrix formed from the eigenvalue analysis of some smaller matrices, which are obtained empirically. (Yes, the previous sentence probably did not make any sense to you, but no worries since the main message is some mathematics is used here.) Empirical here means someone will be using their gut feeling to assign some scores based on comparisons between elements.
Although ANP can be quite complex if one intends to understand the topic at a low level, it is helpful to mentally model it as a kind of black box.
ANP as Blackbox: In essence, ANP takes input from various criteria, alternatives, their interdependencies, and a good measure of a gut feeling as mentioned. The internal mechanics of the ANP method consider these interrelationships, performing complex calculations to determine how the influence of each criterion propagates throughout the network. The output—the array of fractions—provides a prioritized ranking of the criteria, allowing decision-makers to understand which factors carry the most weight in reaching the desired outcome. The system works seamlessly, producing these weights even when the network structure is intricate and the interdependencies are non-linear.
Returning to the MAR site suitability issue, completing the network creation process is sufficient for us, as network analysis can be handled by software programs like Super Decisions. Therefore, we propose the following network based on a review of the scientific literature on MAR (Fig. b).
The proposed network consists of nodes representing the overall goal of finding a suitable MAR site and four distinct clusters, each containing several criteria that share a common theme. The arrows emerging from the goal indicate that it is influenced by the four clusters: soil properties, water resources, geological factors, and environmental and urban considerations. Within these clusters, there are also internal dependencies, meaning some criteria are interrelated. For example, an arrow from aquifer transmissivity to unsaturated zone thickness suggests that aquifer transmissivity—which depends on its hydraulic conductivity and thickness—can mean something about the thickness of the unsaturated zone by dictating how quickly water moves through the system. Similarly, the distance to urban areas influences the distance to roads, as proximity to urban areas often correlates with the proximity to roads.
In Summary, users will only need to specify the geographical location area to determine its suitability for the MAR site. The analyzer will take care of the complex relationship between the area’s different unique features, slopes, soil texture, etc…, and show the users which areas are most suitable for building MAR.
Note: For more in-depth treatment of the theory behind ANP, please check out [7].
Climate prediction model
Even with advanced analytical methods, a significant challenge remains: the dynamic nature of data. The ever-changing nature of climate-related data requires continuous adaptation and innovation in our approaches to determine suitable MAR sites.
This is where we bring in the climate model prediction. The climate prediction models will allow us to account for future data feature changes in analyzing the suitability of MAR.
Let’s see how it looks in action
Users will be able to specify the geographical area they would like to analyze for MAR suitability and get the answers right away.
The climate model highlights the percentage differences in predictions compared to the most recent data on key features. Users will be able to view a combined suitability score of MAR displayed on a map, along with individual maps showing the suitability scores for each feature.
Business model
Our target customers will be companies that specialize in Engineering, Infrastructure consulting, and Water cycle management as well as government tenders. We will offer our solution as SaaS for them to use as an analytical tool. Through our solutions, users will be able to facilitate their work progress, save costs, and gain a competitive advantage.
Each user is unique. And since our solution’s main driving dynamic cost will be the computing resources, the users will be charged based on the computing resources they have used through the analyzer.
Team
Our team is composed of diverse individuals with significant experience in software development, AI&ML engineering, and mathematics.
References
[1] https://gw-project.org/the-importance-of-groundwater/
[2] https://www.un.org/en/global-issues/population
[3] https://www.worldbank.org/en/news/infographic/2022/03/23/groundwater-vital-but-invisible
[4] Jasechko, S., Seybold, H., Perrone, D. et al. Rapid groundwater decline and some cases of recovery in aquifers globally. Nature 625, 715–721 (2024). https://doi.org/10.1038/s41586-023-06879-8
[5] Alam, Sarfaraz, et al. “Managed aquifer recharge implementation criteria to achieve water sustainability.” Science of the Total Environment 768 (2021): 144992.
[6] https://research.csiro.au/mar/
[7] Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software.