To effectively address the multifaceted challenges posed by climate change, it is crucial to comprehend its various dimensions. In particular, the impact of climate change is closely linked to water, accounting for 90% of its consequences. The extremes of excessive water, resulting in flooding, and insufficient water, causing drought, have a substantial impact on both food security and water availability. Therefore, a thorough understanding of water dynamics becomes imperative for effective adaptation and mitigation strategies.
Deep neural networks have shown great empirical success in the last years, but their inner working often remained elusive. XAI methods aim to open up the black box of such opaque models at the heart of many modern machine learning and artificial intelligence applications.
Data is often considered the lifeblood of machine learning algorithms. The availability of vast amounts of data has played a vital role in the rapid development of AI applications across various domains. However, a significant challenge that researchers and practitioners face in the AI landscape is the issue of data scarcity. Data scarcity refers to the limited availability or inadequacy of high-quality data for training AI models.