Water faces serious threats, and leaks drive the need for innovative solutions. In Anabta, 40% of water is lost due to leakage (Anabta Municipality, 2021), including a 20-year-old undetected leak that caused street collapse. In the West Bank, non-revenue water reached 62% in Anata due to illegal connections, and 51–55% in other areas like Jenin and Bal’a due to leaks. In Gaza, losses reached 58% in Al-Maghraqa. Globally, Bangalore and Tokyo suffer significant leak-related losses.

Our robot, EDM, uses Arduino WeMos D1 with sensors along pipelines and a Raspberry Pi mobile robot with a camera and servo arm to detect and remove invasive roots. Data is sent via network to authorities, and leaks trigger automatic pump shutdown—advancing SDG 6 and SDG 11 in Palestine.

This research presents a next-generation AI-powered rapid water testing system designed for real-time, high-precision water quality monitoring in both industrial and domestic settings. At its core is the ESP32-C6 microcontroller, integrated with a 1.47-inch TFT display for immediate, on-site data visualization. The device utilizes five complementary analytical techniques; capacitance measurement, resistance analysis, ultraviolet (UV) exposure, infrared (IR) absorption, and Raman spectroscopy to evaluate water quality within seconds. These multi-modal sensing approaches enable the system to detect a wide spectrum of contaminants. Capacitance and resistance measurements help identify inorganic ions, dissolved salts, and microbial presence. UV and IR absorption offer rapid insights into organic pollutants, while Raman spectroscopy provides detailed molecular fingerprinting. Collectively, these techniques allow the estimation of key indicators such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), total coliforms, and fecal coliforms. The system can also identify heavy metals, synthetic dyes, microplastics, and pathogens like E. coli in real time. An embedded AI model, trained on a large and diverse dataset of water samples, interprets the data to detect complex contamination patterns with high reliability. This results in a portable, energy-efficient, and cost-effective solution capable of delivering immediate water quality assessments, empowering users to make proactive, informed decisions regarding water safety. However, the system does have certain limitations. It cannot measure all possible water quality parameters such as specific pesticide residues with high accuracy due to the constraints of sensor selectivity and hardware resolution. Moreover, while the device performs well in a broad range of scenarios, it may require calibration or additional modules to ensure accuracy across highly specialized or extreme conditions.

Many cities suffer the consequences of flooding after a large amount of water
falls in a short period of time. Therefore, this project determines how urban
vegetation modifies soil structure, improving its water infiltration capacity and
reducing flood damage, putting Nature-Based Solutions (NBS) into practice.

A humidity sensor was built using Arduino and the humidity and water
infiltration capacity of soils influenced by five tree species and seven different shrub
species were measured. Analysis of the results obtained shows that areas
occupied by shrubs infiltrate a greater amount of water. In this sense, species such
as rosemary (Salvia rosmarinus) and oleander (Nerium oleander) stand out for the
development of their root system.

With this information, an image recognition model was created and trained
using LearningML, capable of identifying the studied plants in situ and indicating the
infiltration capacity of the surrounding soil. From it, a Geographic Information
System (GIS) was generated using the QGIS program, which shows the areas of our
city most vulnerable to torrential rains and flooding.

Many cities suffer the consequences of flooding after a large amount of water
falls in a short period of time. Therefore, this project determines how urban
vegetation modifies soil structure, improving its water infiltration capacity and
reducing flood damage, putting Nature-Based Solutions (NBS) into practice.

A humidity sensor was built using Arduino and the humidity and water
infiltration capacity of soils influenced by five tree species and seven different shrub
species were measured. Analysis of the results obtained shows that areas
occupied by shrubs infiltrate a greater amount of water. In this sense, species such
as rosemary (Salvia rosmarinus) and oleander (Nerium oleander) stand out for the
development of their root system.

With this information, an image recognition model was created and trained
using LearningML, capable of identifying the studied plants in situ and indicating the
infiltration capacity of the surrounding soil. From it, a Geographic Information
System (GIS) was generated using the QGIS program, which shows the areas of our
city most vulnerable to torrential rains and flooding.

In view of the increasing number of heavy rainfall events, we have developed WarnMe – a flood warning system. It records water levels in real time using infrared and ultrasound sensors and measures flow velocity via radar. The data is sent to our server via LoRaWAN, where it is analyzed using intelligent algorithms. A user-friendly app ensures that users are immediately informed of potential hazards. Simple installation instructions enable independent setup without professional assistance, creating a decentralized, cost-effective, and scalable system. In summer 2024, WarnMe successfully detected an imminent flood event and contributed to public safety by issuing timely warnings to the affected population.

FLOODWISE is an innovative automated flood management system designed to mitigate flooding and repurpose water efficiently. The system integrates a flood indicator, a biofiltration setup, and an eco roof. These components work in synergy to convert floodwater and rainwater into usable water for non-potable purposes such as washing and flushing. By combining real-time flood monitoring with sustainable water treatment and storage solutions, FLOODWISE aims to enhance urban water resilience and promote eco-friendly water usage practices.

This project aims to prevent further melting of Arctic ice through the creation of a bioengineered strain of glacial Cryobacterium that can uptake dissolved silicon, and biomineralize it into silica (glass), to increase the reflectivity of glaciers. This novel, environmentally-friendly solution will lead to an increase in reflectance that will alow the glaciers to recover over time and prevent devastating sea level rises that would occur due to climate change. 

The problem of saltwater and flash floods devastates the world’s food security. Statistics indicate that agricultural crop yields are affected by saltwater intrusion up to 20% of the land area, with projections reaching 50% by 2050. Flash flood problems have affected the settlement of people in agricultural and community areas. The Intelligent Systems to Forecast and Manage Saltwater Intrusion and Flash Floods for Agriculture, Water Transportation, and Community was developed to address these problems with adequate early warnings and water management. The accuracy of the innovation to forecast saltwater intrusion and flash floods one day and one week earlier, with 70% and 90%, respectively. A smart innovation guaranteeing sustainable agricultural production, global food security and sustainable cities and communities.

The implementation of Movement Control Order (MCO) during the COVID-19 pandemic in 2020-2021 has affected the production process of agricultural produce due to labour shortage. Agricultural workers were unable to come to work to care for crops. At the same time, the MCO has also given opportunities for budding farmers who began to show interest in gardening and making profit.

Starting from mid-2020, our school has established a small farm using fertigation farming to generate income. Fertigation, formed by two words – fertilization and irrigation; is a concept that relies on using the present irrigation line operating in existing field to inject plants with the desired fertilizers. From this idea, we have designed and proposed an automatic plant watering system, an innovative technology to make farmers work more efficiently and yield more profit.

The irrigation technics currently applied in farms are inefficient and causing excessive volume of water wastage. Artificial intelligence system that is applied in agriculture; also known as precision farming, may help farmers to efficiently control water usage thus produce a profitable crop.

Our project on automatic plant watering system named Mechatronic Fertigation, is a device system that transmits data from soil-moisture sensor to inform decisions about watering schedules besides supporting the efficiency of fertilizers in mass production of agriculture.

If moisture in the soil is considered at the optimum amount, plants can wealthily absorb water. The data obtained from the Mechatronic Fertigation helps farmers to increase their profit by learning how to take care of their crops and determining the ideal amount of water and fertilizer to use. By allowing humans to grow food in urban areas, this technology may have the capacity to reduce deforestation.