Scientific tracks

Themes

💧 01

Digital Transformation and Management of Water, Sanitation and Agriculture

Digital tools, integrated management, precision agriculture and WASH governance.

This track explores the digital transformation of water, sanitation and agriculture through hydrometeorological platforms, early warning systems, remote sensing, hydrological and hydraulic models, sensors, climate databases and decision-support tools. It also emphasises digital inclusion so that generated knowledge becomes accessible to technical services, local authorities and communities.

1.1. Digital Tools and Integrated Water Resource Management

Floods and droughts are major hydroclimatic risks in vulnerable urban, peri-urban and rural areas. Real-time hydrometeorological platforms, early warning systems, remote sensing, hydrological and hydraulic models and climate databases improve the understanding of watershed dynamics and support evidence-based decision-making.

1.2. Precision Agriculture and Sustainable Agricultural Water Management

Precision agriculture relies on soil moisture sensors, connected weather stations, satellite imagery, GIS and agro-hydrological models to optimise irrigation at plot scale. In developing countries, a key challenge is extending access to smallholder farmers through mobile apps, agricultural SMS and tailored training.

1.3. Digital Innovation for Urban and Peri-Urban Water and Sanitation

Urban water and sanitation management faces demographic growth, unplanned urbanisation, pollution and infrastructure deficits. Digital technologies enable real-time network monitoring, leak detection, smart treatment-plant management, sanitary risk mapping and citizen participation.

🤖 02

Open Data, AI and Participatory Science for Research and Innovation

Open data, artificial intelligence, big data and knowledge co-production.

This track addresses open data, interoperability, metadata structuring, artificial intelligence, big data, predictive modelling and participatory approaches. It aims to strengthen water governance, risk anticipation, institutional transparency and knowledge co-production among scientists, citizens, farmers and local stakeholders.

2.1. Open Data and Water Governance

Open data is a strategic lever for transforming institutional frameworks and governance modes. It requires standardisation, rigorous metadata, infrastructure interoperability and cross-sector integration to avoid information silos and make results comparable across territories.

2.2. Artificial Intelligence and Big Data for Water Risk Management

Machine learning, predictive modelling and advanced time-series analysis use hydrological, climatic, satellite-based and IoT datasets to improve early warning, forecasting, irrigation optimisation and decision-support tools. Robustness, transparency and ethics remain central issues.

2.3. Participatory Science and Co-Production of Knowledge

Sustainable water management requires both scientific knowledge and local expertise. Participatory platforms, collaborative data collection and the involvement of citizens, farmers and local actors strengthen scientific legitimacy, institutional accountability and the production of territorially relevant data.

🎓 03

Artificial Intelligence and Optimisation of Educational Systems

Adaptive learning, educational data and inclusive digital ecosystems.

This track analyses how artificial intelligence can transform African education systems through adaptive learning, performance monitoring, data-based planning, inequality reduction, low-bandwidth solutions, offline functionality and digital skills development. The objective is to identify responsible, contextualised and sustainable uses of AI in education.

3.1. Intelligent Adaptive Learning Systems and Academic Performance

Adaptive learning systems adjust learning environments to individual needs, pace and cognitive profiles. They provide personalised pathways, immediate feedback and targeted remediation, helping reduce performance gaps in contexts with high student-to-teacher ratios.

3.2. Strategic Use of Educational Data for Planning and Decision-Making

The strategic use of educational statistics, academic performance data and socioeconomic indicators helps identify trends, evaluate programme effectiveness and anticipate infrastructure or staffing needs, supporting targeted policy actions.

3.3. Sustainable and Equitable Digital Educational Ecosystems

Integrating digital and AI technologies into African education systems requires a systemic approach: infrastructure, regulatory frameworks, digital skills, low-bandwidth applications, offline functionality and accessibility on widely available mobile devices.

⚙️ 04

Innovative Processes and Smart Technologies in Water and Sludge Treatment

Faecal sludge, wastewater, membranes, leaks and smart networks.

This track covers smart faecal sludge treatment, decentralised wastewater systems, by-product valorisation, digital membrane modelling, automated water quality monitoring and machine learning for leak detection and drinking-water network management.

4.1. Smart Faecal Sludge Treatment and By-Product Valorisation

Smart technologies such as modelling, sensors, automation and AI can improve faecal sludge treatment efficiency, control flows and create economic opportunities from by-products, especially in African cities.

4.2. Innovative Decentralised Wastewater Management

Source-separation approaches enable treatment adapted to effluent characteristics and open pathways for water, energy and fertiliser reuse. Modular integrated systems are promising for dense urban areas without centralised infrastructure.

4.3. Digital Modelling of Membrane Performance for Drinking Water Treatment

Digital tools for predicting membrane performance simulate particle and ion behaviour within membranes and account for physicochemical interactions. AI integration refines these models and opens perspectives for automated monitoring.

4.4. Machine Learning for Leak Detection and Drinking Water Network Management

Complex drinking-water networks can be better managed through flow, pressure and consumption data. Machine-learning approaches coupled with hydraulic modelling help detect anomalies, predict failures and reduce losses.

Special Sessions

Special sessions

Cross-Perspectives Debate: Artificial Intelligence vs. Classical Approaches to Predicting Natural Phenomena

Complementarity or opposition?

This panel highlights the evolution of forecasting methods in hydrology, agriculture and environmental science. Speakers will compare the performance, limitations and application domains of physical, statistical and empirical approaches with AI-based methods for predicting floods, droughts, extreme rainfall, crop yields and other natural phenomena. Discussions will address data reliability, model interpretability, the role of human expertise and ethical issues around AI.

UNESCO-IHE / WDPP Special Session

Water diplomacy, transboundary governance and capacity building in West Africa

This session will present the Water and Development Partnership Programme, its opportunities for professionals, results from supported projects in West Africa and potential synergies between West African universities and IHE Delft. Discussions will focus on water diplomacy, transboundary governance, equity, ecological sustainability, financing and strengthening the WDPP alumni network in Africa.