Sustainable and effective management of urban water supply is a key challenge for the well-being and security of current society. Urban water supply systems have to deal with a huge amount of data, and it is difficult to develop efficient intervention mechanisms by relying on the human experience. Deep learning methods make it possible to predict water demand in real-time; however, deep learning methods have a large number of hyperparameters, and the selection of hyperparameters can easily affect the accuracy of prediction. Within this context, a novel framework of short-term water demand forecast is proposed, in which a forecasting method clouded leopard algorithm based on multiple adaptive mechanisms—long short-term memory networks (MACLA-LSTM)—is developed to improve the accuracy of water demand predictions. Specifically, LSTM networks are used to predict water demand and the MACLA is utilized to optimize the input parameters of the LSTM. The MACLA-LSTM model is evaluated on a real dataset sampled from water distribution systems. In comparison with other methods, the MACLA-LSTM achieved MAE values of 1.12, 0.89, and 1.09; MSE values of 2.22, 1.21, and 2.38; and R2 values of 99.51%, 99.44%, and 99.01%. The results show the potential of the MACLA-LSTM model for water demand forecasting tasks and also demonstrate the positive effect of the MACLA on forecasting tasks by comparing results with LSTM variant models. The proposed MACLA-LSTM can provide a resilient, sustainable, and low-cost management strategy for water supply systems.

Accurate and reliable wind direction prediction is important not only for enhancing the efficiency of wind power conversion and ensuring safe operation, but also for promoting sustainable development. Wind direction forecasting is a challenging task due to the random, intermittent and unstable nature of wind direction. This paper proposes a short-term wind direction prediction model based on quadratic decomposition and long short-term memory (LSTM) to improve the accuracy and efficiency of wind direction prediction. Firstly, the model adopts a seasonal-trend decomposition procedure based on the loess (STL) method to divide the wind direction series into three subsequences: trend, seasonality and the remainder, which reduces the impact of the original sequence’s complexity and non-stationarity on the prediction performance. Then, the remainder subsequence is decomposed by the optimal variational mode decomposition (OVMD) method to further explore the potential characteristics of the wind direction sequence. Next, all the subsequences are separately input into the LSTM model, and the prediction results of each subsequence from the model are superimposed to obtain the predicted value. The practical wind direction data from a wind farm were used to evaluate the model. The experimental results indicate that the proposed model has superior performance in the accuracy and stability of wind direction prediction, which also provides support for the efficient operation of wind turbines. By developing advanced wind prediction technologies and methods, we can not only enhance the efficiency of wind power conversion, but also ensure a sustainable and reliable supply of renewable energy.

The advancement of the Internet of Things has positioned intelligent water demand forecasting as a critical component in the quest for sustainable water resource management. Despite the potential benefits, the inherent non-stationarity of water consumption data poses significant hurdles to the predictive accuracy of forecasting models. This study introduces a novel approach, the Robust Adaptive Optimization Decomposition (RAOD) strategy, which integrates a deep neural network to address these challenges. The RAOD strategy leverages the Complete Ensemble Empirical Mode Decomposition (CEEMD) to preprocess the water demand series, mitigating the effects of non-stationarity and non-linearity. To further enhance the model’s robustness, an innovative optimization algorithm is incorporated within the CEEMD process to minimize the variance in multi-scale arrangement entropy among the decomposed components, thereby improving the model’s generalization capabilities. The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. To validate the effectiveness of the RAOD strategy, real-world datasets from four distinct geographical regions are employed for multi-step ahead predictions. The experimental outcomes demonstrate that the RAOD model outperforms existing models across all considered metrics, highlighting its suitability for accurate and reliable water demand forecasting in the context of sustainable energy management.

One of the significant features of extreme learning machines (ELMs) is their fast convergence. However, in the big data environment, the ELM based on the Moore–Penrose matrix inverse still suffers from excessive calculation loads. Leveraging the decomposability of the alternating direction method of multipliers (ADMM), a convex model-fitting problem can be split into a set of sub-problems which can be executed in parallel. Using a maximally splitting technique and a relaxation technique, the sub-problems can be split into multiple univariate sub-problems. On this basis, we propose an adaptive parameter selection method that automatically tunes the key algorithm parameters during training. To confirm the effectiveness of this algorithm, experiments are conducted on eight classification datasets. We have verified the effectiveness of this algorithm in terms of the number of iterations, computation time, and acceleration ratios. The results show that the method proposed by this paper can greatly improve the speed of data processing while increasing the parallelism.

In a class of large-scale distributed optimization, the calculation of RELM based on the Moore–Penrose inverse matrix is prohibitively expensive, which hinders the formulation of a computationally efficient optimization model. Attempting to improve the model’s convergence performance, this paper proposes a low computing cost Alternating Direction Method of Multipliers (ADMM), where the original update in ADMM is solved inexactly with approximate curvature information. Based on quasi-Newton techniques, the ADMM approach allows us to solve convex optimization with reasonable accuracy and computational effort. By introducing this algorithm into the RELM model, the model fitting problem can be decomposed into a set of subproblems that can be executed in parallel to achieve efficient classification performance. To avoid the storage of expensive Hessian for large problems, BFGS with limited memory is proposed with computational efficiency. And the optimal parameter values of the step-size search method are obtained through Wolfe line search strategy. To demonstrate the superiority of our methods, numerical experiments are conducted on eight real-world datasets. Results on problems arising in machine learning suggest that the proposed method is competitive with other similar methods, both in terms of better computational efficiency as well as accuracy.

Accurate segmentation of clustered microcalcifications in mammography is crucial for the diagnosis and treatment of breast cancer. Despite exhibiting expert-level accuracy, recent deep learning advancements in medical image segmentation provide insufficient contribution to practical applications, due to the domain shift resulting from differences in patient postures, individual gland density, and imaging modalities of mammography etc. In this paper, a novel framework named MLN-net, which can accurately segment multi-source images using only single source images, is proposed for clustered microcalcification segmentation. We first propose a source domain image augmentation method to generate multi-source images, leading to improved generalization. And a structure of multiple layer normalization (LN) layers is used to construct the segmentation network, which can be found efficient for clustered microcalcification segmentation in different domains. Additionally, a branch selection strategy is designed for measuring the similarity of the source domain data and the target domain data. To validate the proposed MLN-net, extensive analyses including ablation experiments are performed, comparison of 12 baseline methods. Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods. Code will be available at https://github.com/yezanting/MLN-NET-VERSON1.