Cloud forecasting plays a crucial role in various fields such as agriculture, energy systems, and air travel. An accurate forecasting system can offer significant benefits by improving decision-making efficiency in these areas. This study investigates the use of Echo State Network (ESN)-based architectures for weather forecasting, focusing on cloud prediction across Central Europe using the CloudCast benchmark, which integrates data from Meteosat satellites and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Two novel techniques are included in this study, evaluated in two different phases. First, the Multi-Reservoir Weighted ESN (MWESN) architecture is proposed, featuring optimized inter-reservoir connections that enhance both the effectiveness and adaptability of the model. This model is evaluated along with advanced ESN architectures, including Multi-Reservoir ESN, Deep ESN among others. Second, the Error-Guided Regional Training (ERT) method is introduced to minimize the computational resources required for forecasting at the pixel level while maintaining high accuracy. Combined, MWESN and ERT demonstrate a 1.41% improvement in accuracy, effectively capturing complex spatio-temporal dynamics while significantly reducing computational demands compared to existing state-of-the-art methods. Additionally, models are tested on low-resource devices such as Raspberry Pi units, illustrating their feasibility for real-world meteorological applications.

Abstract Echo state networks (ESNs) belong to the class of recurrent neural networks and have demonstrated robust performance in time series prediction tasks. In this study, we investigate the capability of different ESN architectures to capture spatial relationships in images without transforming them into temporal sequences. We begin with three pre-existing ESN-based architectures and enhance their design by incorporating multiple output layers, customising them for a classification task. Our investigation involves an examination of the behaviour of these modified networks, coupled with a comprehensive performance comparison against the baseline vanilla ESN architecture. Our experiments on the MNIST data set reveal that a network with multiple independent reservoirs working in parallel outperforms other ESN-based architectures for this task, achieving a classification accuracy of 98.43%. This improvement on the classical ESN architecture is accompanied by reduced training times. While the accuracy of ESN-based architectures lags behind that of convolutional neural network-based architectures, the significantly lower training times of ESNs with multiple reservoirs operating in parallel make them a compelling choice for learning spatial relationships in scenarios prioritising energy efficiency and rapid training. This multi-reservoir ESN architecture overcomes standard ESN limitations regarding memory requirements and training times for large networks, providing more accurate predictions than other ESN-based models. These findings contribute to a deeper understanding of the potential of ESNs as a tool for image classification.

Children younger than 3 years old are very special humans, their psychomotor and social development is very fast and parents and relatives would like to know every new detail (when, who, where, what, how and why) in real time. These news are difficult to remember and some kind of diary is needed. Here we propose a "prosthetic memory" based on Digital Object Memories applied to Web of Things using hidden NFC tags in children's clothes, mobile applications for smartphones and a central server to store the ontologized information.