AS-SOMTF: A novel multi-task learning model for water level prediction by satellite remoting^✩

Abstract

Satellite communication technology has emerged as a key solution to address the challenges of data transmission in remote areas. By overcoming the limitations of traditional terrestrial communication networks, it enables long-distance data transmission anytime and anywhere, ensuring the timely and accurate delivery of water level data, which is particularly crucial for fishway water level monitoring. To enhance the effectiveness of fishway water level monitoring, this study proposes a multi-task learning model, AS-SOMTF, designed for real-time and comprehensive prediction. The model integrates auxiliary sequences with primary input sequences to capture complex relationships and dependencies, thereby improving representational capacity. In addition, a novel time- series embedding algorithm, AS-SOM, is introduced, which combines generative inference and pooling operations to optimize prediction efficiency for long sequences. This innovation not only ensures the timely transmission of water level data but also enhances the accuracy of real-time monitoring. Compared with traditional models such as Transformer and Long Short-Term Memory (LSTM) networks, the proposed model achieves improvements of 3.8% and 1.4% in prediction accuracy, respectively. These advancements provide more precise technical support for water level forecasting and resource management in the Diqing Tibetan Autonomous Prefecture of the Lancang River, contributing to ecosystem protection and improved operational safety.

Keywords

Fish passages / Water-level prediction / Time series forecasting / Multi-task learning / Hierarchical clustering / Satellite communication

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