Machine Learning for Flood Prediction and Resilience: Leveraging Geomatics in the Digital Age

GEOFREY KUDAKWASHE MAWERE

kudamawere7@gmail.com

MIDLANDS STATE UNIVERSITY

OVERVIEW OF GEOMATICS

• Geomatics is the science and technology of acquiring, analysing, and managing spatial and geographic data
• At its core, geomatics focuses on the collection and interpretation of spatial data, which includes
• information about the location,
• shape,
• size,
• Attributes of objects or phenomena on the Earth's surface. This data can come from a variety of sources, such as satellite imagery, aerial photography, LiDAR, surveying instruments, and sensor networks.

IMPORTANCE OF GEOMATICS

• Geomatics plays a pivotal role in understanding and managing complex environmental phenomena by providing tools and technologies such as

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• remote sensing,
• Geographic Information Systems (GIS),
• Global Navigation Satellite Systems (GNSS)

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• These tools enable us to capture, analyse, and visualize spatial data, leading to valuable insights and informed decision-making.

GLOBAL FLOOD IMPACTS

• (Wang et al.,2022) about 23% of the world’s population (about 1.81 billion people) are under the threat of once-in-a-century flood.

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• The global economic losses caused by floods reached USD 82 billion in 2021

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• Accounting for approximately 31% of the global economic losses related to natural disasters

CONT….

• According to the World Bank Group(2021)
• From 1980 – 2020, Zimbabwe has experienced several riverine floods(Muzarabani, chimanimani and Gururve).

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• During the same time period, 9 riverine floods were accounted, affecting over 300 thousand people

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• killing over 270 people

• above $270 million monetary loss

IMPORETANCE OF FLOOD PREDICTION AND RESILIENCE

• Flood prediction and resilience are of paramount importance in mitigating the devastating impacts of flooding events.

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• Due to climate change and rapid urbanization, floods are becoming more frequent and severe, posing significant economic, social, and environmental challenges.

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• Floods can result in loss of lives, damage to infrastructure, disruption of essential services, displacement of communities, and long-term environmental degradation

INTRODUCTION TO MACHINE LEARNING

• Machine learning is a subset of artificial intelligence that enables computers to learn patterns from data without being explicitly programmed

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• Significance of machine learning: By leveraging machine learning algorithms, we can analyse large datasets, identify patterns, and extract meaningful insights that can aid in flood prediction and resilience efforts.

SUPERVISED LEARNING

• is a common machine learning approach used in vulnerability assessments.

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• In this approach, models are trained on labelled data, where each example is associated with a known vulnerability level.

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• By learning from these labelled examples, the models can generalize patterns and make predictions on new, unlabelled data.

UNSUPERVISED LEARNING

• is a machine learning approach where the algorithm learns from unlabelled data, without any predefined output labels.

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• The goal is to discover underlying patterns, structures, or relationships within the data without explicit guidance.

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• Unsupervised learning algorithms aim to find meaningful representations, clusters, or associations in the data.

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• can also be employed to identify hidden patterns and subgroups within the data. Clustering algorithms, such as k-means or hierarchical clustering.

APPLICATION OF MACHINE LEARNING IN FLOOD PREDICTION

• Machine learning techniques have proven to be effective in improving flood prediction and early warning systems

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• Machine learning excels in handling complex and nonlinear relationships in flood data

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• Commonly used machine learning algorithms in flood prediction include
• neural networks,
• support vector machines
• random forests.

LEVERAGING GEOMATICS IN FLOOD PREDICTION

• Geomatics data and tools enhance the accuracy and effectiveness of flood prediction models.

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• Remote sensing data, such as satellite imagery, helps in monitoring and mapping flood-prone areas, enabling us to identify potential risks and vulnerabilities.

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• By integrating geospatial data from various sources with machine learning algorithms, we can achieve improved accuracy and reliability in flood prediction.

ENHANCING FLOOD RESILIENCE WITH MACHINE LEARNING AND GEOMATICS

• Machine learning and geomatics contribute to enhancing flood resilience by providing valuable insights for proactive measures.

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• Geospatial data assists in identifying vulnerable areas and informs land-use planning and infrastructure development, thereby reducing the exposure to flood risks.

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• Machine learning enables the development of adaptive flood management strategies based on real-time data and predictive modelling, facilitating effective response and recovery.

ALIGNING WITH SUSTAINABLE DEVELOPMENT GOALS(SDGs)

• The use of machine learning and geomatics for flood prediction and resilience aligns with several Sustainable Development Goals (SDGs)
• Goal 11 (Sustainable Cities and Communities),
• Goal 13 (Climate Action), and
• Goal 15 (Life on Land) are particularly relevant to our efforts in flood prediction and resilience.
• By applying these technologies, we can contribute to achieving targets related to disaster risk reduction, climate resilience, and sustainable urban development

SUCCESS STORIES

• Nigeria

Nigeria has made progress in using machine learning for flood prediction. The Nigerian Hydrological Services Agency (NIHSA) has developed flood prediction models that incorporate machine learning algorithms.

These models have shown improved accuracy in forecasting floods and have been utilized to issue timely warnings to vulnerable communities.

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• South Africa

Has been at the forefront of leveraging machine learning for flood prediction. The Council for Scientific and Industrial Research (CSIR) and academic institutions like the University of Cape Town have developed machine learning models that have demonstrated success in predicting floods with increased accuracy. These models have contributed to more effective flood preparedness and response strategies.

CONT….

• The University of Zimbabwe and Midlands State University have been actively involved in research related to flood prediction using machine learning techniques.
• Researchers have been working on developing models that can analyse rainfall patterns, river flow data, and other relevant variables to predict floods. These models aim to provide early warnings and assist in disaster preparedness and response.
• With geomaticians involved these researches can be a success and be implemented in all flood prone areas across Zimbabwe

CONCLUSION

• In conclusion, the integration of machine learning and geomatics offers immense potential in addressing flood prediction and resilience challenges in the digital age.

CALL FOR ACTION

• Let us embrace these technologies and collaborate further to unlock their full potential in safeguarding

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• communities,
• infrastructure, and
• the environment from the devastating impacts of floods.