Senior ResearcherContact Olof
Climate change is affecting the hydrological conditions of the Earth, causing both draught and flooding in different regions. Sweden is facing the challenge of adapting to a warmer and wetter climate, which will lead to more extreme weather events and increase the risks and damages of flooding.
Moreover, the historical drainage of wetlands and straightening of streams have disturbed the natural water cycle and exacerbated the effects of extreme weather events.
To cope with these challenges, we need to understand how water flows in the environment and how it is influenced by various factors, such as rainfall, temperature, land cover, soil, and elevation. This can help us make informed decisions about nature-based climate adaptation techniques, such as restoring wetlands, greening urban areas, and protecting soil. However, traditional hydrological models that rely on expert knowledge and physical properties are limited in their ability to generalize and capture the complexity and variability of water flow dynamics.
In this project, we propose a novel machine learning (ML) approach for predicting water flow intensity that leverages both temporal and spatial data. We use a fully convolutional neural network (FCN) that receives spatio-temporal inputs and predicts the water flow intensity at every coordinate for the next day. Our approach has two main advantages:
The approach has been evaluated on a dataset of water flow intensity measurements from catchment areas in Sweden, covering different climatic and environmental conditions. The evaluation included ablation studies and analysis of the improtance of different factors.
The approach shows strengths in performance, is highly flexible in the information sources that can be provided as inputs to the model, and gives a dense prediction with flow estimations in every point of the input map. We also show some qualitative examples of the predictions made by our model, which illustrate how the model captures the spatial patterns and variations of water flow intensity in different catchment areas.
Our project demonstrates the potential of using ML for water flow intensity prediction, which can provide valuable information for climate adaptation and water management. By using both temporal and spatial data, our model can learn the complex and dynamic interactions between water flow and the environment, and produce high-resolution predictions that can reveal the spatial distribution and heterogeneity of water flow intensity. This can help us better understand the flood risks and the effects of flood and drought mitigation, as well as the general hydrological implications of changes in land use.
Our project also opens up new directions for future research and development, such as:
AI for water flow prediction