Explainable Optimized LSTM Model for Sustainable Wind Power Forecasting in Smart Cities

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit.

The objective of this study is to systematically compare the predictive performance of Long Short- Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and a Hybrid LSTM–XGBoost model for next-day Bitcoin (BTC–USD) closing-price forecasting. The research method employs a quantitative time-series modeling approach using a decade-long daily Bitcoin price dataset. A strictly chronological train–test split and a one-step-ahead forecasting scheme are applied to prevent lookahead bias and ensure experimental validity. Model performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sMAPE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination R2 on the original price scale. The results demonstrate that the Hybrid LSTM–XGBoost model consistently outperforms the standalone LSTM and XGBoost models across all evaluation metrics, indicating superior predictive accuracy and robustness under high market volatility. The contribution of this study lies in providing a controlled, uniform, and methodologically rigorous head-to-head comparison of deep learning, machine learning, and hybrid architectures for Bitcoin price forecasting, thereby enriching the empirical literature and offering a reliable foundation for the development of adaptive decision-support systemsin volatile cryptocurrency investment environments.

This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making.

This chapter aims to explore the application of Internet of Things (IoT) and machine learning (ML) in assessing the planning and management of smart cities and new towns. Additionally, we have discussed the possible solutions that combine IoT and ML opportunities in this field. Similarly, our results support that ML and IoT optimize resources, address environmental challenges and enhance citizen well-being by using real-time data and predictive models. However, challenges such as interoperability, cybersecurity, data protection, algorithmic bias and high costs must be addressed. Finally, we support that by adopting transparent governance and innovation, smart cities and new towns can build resilient urban ecosystems that prioritize the needs of their residents and ensure equitable development.

Prediction OPEC oil price utilizing long short-term memory and multi-layer perceptron models

A smart city has changed the city landscape with the assistance of ICT, and sensor-enabled internet of things (IoT) devices. The development of a ubiquitous connected source environment is to be complicated and global to improve service performance delivery, cost-effectiveness, and using all capabilities of technology resources. For gaining those benefits in the smart city, it is necessary to design a capable ICT architecture. The ICT architecture in the smart city must provide the facility to organize all technology resources, the produced data, network communication orchestration, and services in the large scale of smart cities. In today’s smart cities, three main different ICT architecture proposals are available, Centralized, Decentralized-to-Centralized, and Distributed-to-Centralized. Centralized ICT architectures are designed based on Cloud technologies. The Decentralized-to-Centralized and Distributed-to-Centralized ICT architectures are using the multiple facilities from the joined benefits of edge to Cloud technologies orchestration. In this paper, we first describe these three ICT architectures in smart cities. Besides, we present our proposed Decentralized-to-Centralized and Distributed-to-Centralized ICT architectures in smart cities. Second, we show how our ICT architecture can be beneficial for Large-Scale ICT management through different use case studies. Finally, we describe the advantages of our ICT architecture and discuss challenges.

It was analyzed the information of databases, encryption algorithms, and information security that are the main components of a Smart City. The problem is the lack of security of public or secret data that is stored in a repository, where there is access to the citizen, local government, and central government. The objective is to define a security prototype and adopt a cryptographic algorithm to mitigate the risks of the database in the management of a Smart City. It was used the deductive method and exploratory research to analyze the information of the reference articles. It turned out a general prototype of security; an algorithm to implement a database for Smart City; an algorithm of data protection expressed in flowcharts. It was concluded that information protected with an encryption algorithm, is a support to be more efficient, reduce costs, reduce the environmental footprint and improve the management of a Smart City.

Explainable highway performance degradation prediction model based on LSTM

A massive wave of urbanisation has grasped both developed and developing nations in the last decade. Various studies showcase the rising trend of rapid urbanisation growth due to demographic shifts, catalysed by numerous global and local parameters. India is professed to house 50 percent of its population in urban areas by 2030. The urban citizen’s rising expectations regarding infrastructure, amenities and safety will dovetail with this phenomenon. To cater to the demands of increased urbanisation, the concept of Smart cities is being advocated as an apt solution. Even so, there is widespread ambiguity about the challenges associated with adopting smart solutions into the existing urban social form. Though there have been many tentative explanations regarding the basic framework of a smart city, there are no known validated definitions. The main notable feature is the use of information and communication technologies (ICTs) within the cyclic dynamics of a city to facilitate the smooth and cost-effective working of urban areas. This ICT infrastructure enables real-time data collection, analysis, response, and storage. This has been deemed beneficial due to the increase in efficiency of operation and management services involved with the day-to-day functioning of any urban area. Smart cities advocate using information and communication technologies (ICTs) within the dynamics of a city to enable the real-time collection, analysis, and storage of big data. This is beneficial due to the increased efficiency of operation and management services involved with an urban area’s daily functioning. One such technological intervention is the ‘drone’ or the ‘unmanned aerial vehicles (UAVs)’. UAVs have a wide variety of uses in a smart urban fabric, from geospatial integration to traffic management, surveillance, disaster response, etc. In the current nascent stage of research and development of UAVs as one of the innovative solutions for smart cities in India, questions arise regarding privacy, cost of production, technical knowledge, safety and security with their large-scale use. This paper aims to assess the applicability of UAVs in overall smart city planning and management. The feasibility analysis method is adopted to analyse the felicitousness of UAVs in a smart city’s planning and design phases. The results of the study undertaken in this paper highlight the challenges and opportunities in the planning and management of smart cities by integrating UAVs. The paper enumerates the relevance and appropriate benefits of using UAVs to plan, design, and perpetuate Indian smart cities.

24 - Current trends and future challenges in smart waste management in smart cities

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems.

A novel anomaly detection method for multivariate time series based on spatial-temporal graph learning

Elucidating and forecasting the organochlorine pesticides in suspended particulate matter by a two-stage decomposition based interpretable deep learning approach

BackgroundHeart failure (HF) represents a pressing global health issue demanding innovative and accessible approaches for early detection. Non-invasive, rapid, and cost-effective techniques utilizing deep learning (DL) hold significant promise for addressing this challenge.MethodsThis study included 462 participants categorized into healthy, coronary artery disease (CAD), and HF groups. Raw radial artery pulse wave data underwent preprocessing, including denoising, normalization, and SMOTE-based balancing. Four deep learning algorithms adept at handling sequential data – Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM) – were subsequently applied and evaluated for their classification performance. A 10-fold cross-validation strategy was employed to ensure a robust evaluation of model performance and stability.ResultsTo robustly evaluate performance and stability, a 10-fold cross-validation was performed. The LSTM model yielded the highest mean accuracy (0.8595 ± 0.0522) and demonstrated strong performance across other key metrics, including a high Area Under the Curve, establishing it as the most effective model in this study. To enhance model interpretability, the SHAP (SHapley Additive exPlanations) framework was utilized to determine global feature importance and explain the final LSTM model’s predictions.ConclusionOur findings strongly suggest that an LSTM-based model analyzing radial artery pulse waves can effectively differentiate between healthy, CAD, and HF states. This approach represents a simple, non-invasive, and cost-effective methodology with significant potential as a valuable strategy to aid in the early screening and detection of HF. Further investigation and broader clinical validation are warranted to confirm the robustness and real-world applicability of this promising tool.