Understanding the combined influences of meteorological and hydrological
factors on water level and flood events is essential, particularly in today's
changing climate environments. Transformer, as one kind of the cutting-edge
deep learning methods, offers an effective approach to model intricate
nonlinear processes, enables the extraction of key features and water level
predictions. EXplainable Artificial Intelligence (XAI) methods play important
roles in enhancing the understandings of how different factors impact water
level. In this study, we propose a Transformer variant by integrating sparse
attention mechanism and introducing nonlinear output layer for the decoder
module. The variant model is utilized for multi-step forecasting of water
level, by considering meteorological and hydrological factors simultaneously.
It is shown that the variant model outperforms traditional Transformer across
different lead times with respect to various evaluation metrics. The
sensitivity analyses based on XAI technology demonstrate the significant
influence of meteorological factors on water level evolution, in which
temperature is shown to be the most dominant meteorological factor. Therefore,
incorporating both meteorological and hydrological factors is necessary for
reliable hydrological prediction and flood prevention. In the meantime, XAI
technology provides insights into certain predictions, which is beneficial for
understanding the prediction results and evaluating the reasonability.

通过结合稀疏注意机制和引入非线性输出层的变体模型，本研究基于变压器模型对水位进行多步预测，并考虑同时的气象和水文因素。结果表明，该变体模型相比传统的变压器模型，在不同的前瞻时间内以各种评估指标显示出更好的性能。基于 XAI 技术的敏感性分析显示出气象因素对水位演变的显著影响，其中温度是最重要的气象因素。因此，同时考虑气象和水文因素对可靠的水文预测和防洪工作至关重要，并且 XAI 技术帮助理解预测结果并评估其合理性。

基于可解释人工智能技术的用于水位多步预测和水文气象敏感性分析的 Transformer 改进型模型

A Transformer variant for multi-step forecasting of water level and  hydrometeorological sensitivity analysis based on explainable artificial  intelligence technology

Predicting solar panel power output is crucial for advancing the energy
transition but is complicated by the variable and non-linear nature of solar
energy. This is influenced by numerous meteorological factors, geographical
positioning, and photovoltaic cell properties, posing significant challenges to
forecasting accuracy and grid stability. Our study introduces a suite of
solutions centered around hybrid quantum neural networks designed to tackle
these complexities. The first proposed model, the Hybrid Quantum Long
Short-Term Memory, surpasses all tested models by over 40% lower mean absolute
and mean squared errors. The second proposed model, Hybrid Quantum
Sequence-to-Sequence neural network, once trained, predicts photovoltaic power
with 16% lower mean absolute error for arbitrary time intervals without the
need for prior meteorological data, highlighting its versatility. Moreover, our
hybrid models perform better even when trained on limited datasets, underlining
their potential utility in data-scarce scenarios. These findings represent a
stride towards resolving time series prediction challenges in energy power
forecasting through hybrid quantum models, showcasing the transformative
potential of quantum machine learning in catalyzing the renewable energy
transition.

通过混合量子神经网络，该研究提出了两种模型来预测太阳能电池板的功率输出，其中第一种模型表现最好，降低了超过 40% 的平均绝对误差和平均平方误差；第二种模型则在没有气象数据的情况下，以比传统模型低出 16% 的平均绝对误差预测光伏功率，展现出量子机器学习在可再生能源领域中的潜在用途。