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With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China's stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China's stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China's stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program. [ABSTRACT FROM AUTHOR]

Landslides cause severe economic damage and a large number of casualties every year around the world. In many cases, it is not possible to avoid them, and the task of engineers and geologists is to mitigate their effects using measures such as building diverting structures or preparing escape roads to be used when an alarm is triggered. It is necessary, therefore, to predict the path of the landslide, its depth and velocity, and the runout. These objectives are usually are attained by using mathematical, numerical and rheological models. An important limitation of the analysis is the lack of data, specially when few laboratory tests are available, and in cases where their present important variations. This leads to performing sensitivity analyses in which analysts study the influence of several magnitudes of interest, such as friction angle, porosity, basal pore pressure and geometry of the sliding mass, just to mention a few, leading in turn to perform a large number of simulations. We propose in this paper a methodology to speed up the process, which is based on: (i) using depth integrated models, which provide a good combination of accuracy and computer effort and (ii) using artificial intelligence tools to reduce the number of simulations. Let us consider a case where we have N mag main variables to explore; for each of them we select a number of cases, which can differ from one magnitude to another. The number of cases will be where N cases (i) is the number of cases we have selected for magnitude i. We can consider these variables as nodes belonging to a hypercube of dimension N mag . We will refer from now on as "hypercube" to the set of all cases generated in this way. The paper presents two cases where these techniques will be applied: (i) a 1D dam break problem and (ii) a case of a real debris flow which happened in Hong Kong, for which there is available information. [ABSTRACT FROM AUTHOR]

A major source of risk in green building projects (GBPs) is inaccurate human prediction of the final project cost and duration, which in turn results in cost and schedule overruns (i.e., poor project performance). This paper presents promising new models to mitigate such risk based upon machine learning (ML). Historical data from 198 GBPs in Hong Kong were used to develop and train two fully connected deep neural networks (DNN) models to learn and predict cost and duration, respectively, based on green building rating (GBR) and other project parameters. The models can predict cost and duration with mean absolute percentage error (MAPE) values of 0.07 and 0.09, respectively. They were then integrated with support vector regression (SVR), and results indicated that the integrated DNN-SVR models improve prediction accuracy, decreasing the MAPE from 0.07 to 0.06 (cost) and 0.09 to 0.07 (duration), respectively. The validated models were for the first time deployed as a ML-based web application for automated, fast, and accurate GBP cost and duration prediction. The feature importance analysis results revealed that the most influential parameters on the GBP cost and duration are project area and weather, respectively, not the GBR. Theoretically, the outcomes of this study provide new insights into the impact of GBR on project cost and duration, which are useful for the promotion of GBPs to improve sustainability. Practically, the study provides policymakers and practitioners with novel ML-based models and a web application to improve GBP delivery performance. Green building projects help to combat climate change and improve our health, wellbeing, and quality of life, but they face two key challenges in their execution: cost overruns and schedule delays. To address these challenges, there is a need for accurate prediction of the final project cost and duration from the early stages of the project. The practical relevance of this study is in the development of the first data-driven and machine learning-based web application for addressing this need. New integrated optimized machine learning models are developed. For practitioners to have access to and use these models without the need to possess machine learning expertise, a corresponding easy-to-use web application is offered. From the design and construction stages of their project, practitioners only need to input the green building ratings in sustainable site, materials and waste, energy use, water use, health and wellbeing, and innovations and additions they want to achieve for the project. The project type and area (type and size), original budget, planned duration, and start month (SM) and year should also be input. Once this project data input is completed, the web application automatically and instantaneously predicts with a high level of accuracy the total costs and time needed to deliver the project successfully—on time and on budget. These cost and duration prediction outputs are a valuable tool in helping practitioners adjust green building project plans and budgets to develop more realistic and accurate project budgets and programs to avoid cost overruns and schedule delays. [ABSTRACT FROM AUTHOR]

Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA's superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities. [ABSTRACT FROM AUTHOR]

Hong Kong International Airport (HKIA) is an important sea airdrome in China. The aim of this study is to evaluate the impacts of this reclamation on the water quality of the Northwestern Bay of Hong Kong (NWBHK). In all, 117 Landsat 5 TM and 44 Landsat 8 OLI images were preprocessed and matched with the marine water data of 18 in situ monitoring points, acquiring 458 and 119 sets of data, respectively. This study adopted BPNN Machine Learning methods to establish the retrieval algorithm. Based on the images, the construction of HKIA was divided into three stages: (1) the construction of the first and second runways from 1992 to 1995; (2) the construction of the Hong Kong Port from 2013 to 2016; and (3) the construction of the third runway from 2017 to 2020. The concentrations of suspended particulate matter, orthophosphate phosphorus, and dissolved inorganic nitrogen from 1986 to 2022 were retrieved. In this paper, it was found that (1) the construction activities led to an increase in SPM, PO4P, and DIN concentrations in adjacent water bodies; (2) the impact of the Tuen Mun River on the NWBHK increased, while the impact of the Tung Chung River on the NWBHK decreased; and (3) the interception impact of HKIA on the transportation of the Pearl River water became stronger. [ABSTRACT FROM AUTHOR]

Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences. Highlights: A next-generation convolutional neural network (CNN) called HKUDES_Net is developed to perform automatic rock classification. HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. By implementing the alerting level in HKUDES_Net, the overfitting has been efficiently eliminated. Plain Language Summary: Rock classification provides vital information to geosciences and geological engineering practices. However, the traditional way of rock classification is to a certain degree experience-based. Computer vision-based artificial intelligence (AI), especially convolutional neural network (CNN), a sub-branch of AI, may help people automatically classify rock types with the hope to train AI models (CNNs), which can instantly and precisely perceive rock images similar to how human beings observe rocks. In other words, by putting a rock image into a CNN, the CNN will read the information from the image and tell people what rock type in the image is. This study develops and implements a next-generation CNN called HKUDES_Net to classify seven different rock types. Adopting different computational strategies, HKUDES_Net can classify rock types of different grain sizes with similar texture patterns/colors, which is challenging for other CNNs. As compared with the other landmark CNNs and machine learning algorithms, HKUDES_Net has much a better prediction accuracy performance. The proposal and implementation of HKUDES_Net exemplify an interdisciplinary research study, and we are confident that this paper will be instrumental to pave the way for further coupling AI and geosciences. [ABSTRACT FROM AUTHOR]

• This paper focused on the reliability of settlement prediction of immersed tunnel. • This paper focused on impact of time-dependent effects on settlement prediction. • A time-dependent expression for foundation modulus is proposed. • Several extreme conditions are considered in the future settlement prediction. The focus of immersed tunnel research has primarily revolved around settlement monitoring or prediction. However, there has been a lack of discussion regarding the time-dependent effects on settlement prediction, which could lead to obvious misjudgments in subsequent maintenance work. This paper addresses this issue by applying the physics-informed machine learning (PIML) algorithm to conduct an inverse analysis of the foundation modulus of the Hong Kong-Zhuhai-Macau Bridge (HZMB) tunnel, thereby identifying its variation tendency with time. The proposed time-dependent expression of foundation modulus demonstrates that the time-dependent effect is related to both post-construction settlement and the accumulation of back silting, which is significant and could persist throughout the entire service life of the HZMB tunnel. In further studies on settlement prediction for the HZMB tunnel, the results show that errors in prediction can exceed 30 mm at certain monitoring points. This error can be mitigated by considering the time-dependent effect, thereby significantly enhancing the reliability of predictions. [ABSTRACT FROM AUTHOR]

This research concentrates on segmenting credit card clients of Taiwan into optimal groups. Unsupervised Learning plays a significant role in dividing customers into similar groups based on several parameters. If customers are clustered in groups optimally, it leads towards the retrieval of better precision from machine learning models applied to customers associated with the clusters. Different machine learning algorithms (Linear discriminant analysis, logistic regression and random forest) were applied on the obtained clusters through K-means, hierarchical and HK Means clustering algorithm, and predictive accuracy is compared with the accuracy obtained via applying mentioned machine learning models on the whole dataset. In this paper, a novel approach of combining K Means and hierarchical clustering (HK Means) is used. In this approach, HK means clustering algorithms are applied on the factorial coordinates, obtained from multiple correspondence analyses for segmenting customers into optimal groups has been proposed. The accuracy of the clustering techniques is evaluated from the decomposition of inertia. The results demonstrate that the combination of K Means and hierarchical clustering proved to be optimal clustering techniques for customer segmentation which can be used further for applying Machine Learning techniques for credit risk analysis. [ABSTRACT FROM AUTHOR]

Intelligently predicting the frequency and volume of natural hazards, including boulder falls, attracts widespread attention in earth science and engineering communities. Taking Hong Kong, China, which is a highly developed and densely populated city, as a research example, the present paper statistically studies the occurrence of boulder falls based on power law relationships and supervised machine learning. The 2002–2016 boulder fall inventory of Hong Kong, which consists of 194 boulder falls, is compiled and analyzed in detail, including volume and spatial distributions. Based on the findings, a power law relationship with the b-value greater than one is particularly proposed for Hong Kong, which then allows major boulder falls (volume ≥1 m3) occurring in two subsequent years to be accurately predicted. In order to predict volume ranges of potential boulder falls, eight supervised machine learning algorithms are trained and validated based on sixty-five filtered samples with five represented features. Two key conclusions are drawn from the findings. First, the geological settings, altitudes, and gradients of source locations are demonstrated to be three triggering factors of different volume ranges of boulder falls. Second, the logistic regression algorithm has the best performance in predicting boulder fall volumes among all the algorithms. The f1-score of 5-fold cross-validation is 72.9%, and the prediction accuracy of the test dataset is 75.8%. We are confident that the methodologies and results in this paper will pave the way for analyzing other types of natural terrain hazards in addition to boulder falls in the long run. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

The dissemination of fake news on social media platforms is an issue of considerable interest, as it can be used to misinform people or lead them astray, which is particularly concerning when it comes to political events. The recent event of Hong Kong protests triggered an outburst of fake news posts that were identified on Twitter, which were then promptly removed and compiled into datasets to promote research. These datasets focusing on linguistic content were used in previous work to classify between tweets spreading fake and real news using traditional machine learning algorithms (Zervopoulos et al., in: IFIP international conference on artificial intelligence applications and innovations, Springer, Berlin, 2020). In this paper, the experimentation process on the previously constructed dataset is extended using deep learning algorithms along with a diverse set of input features, ranging from raw text to handcrafted features. Experiments showed that the deep learning algorithms outperformed the traditional approaches, reaching scores as high as 99.3% F1 Score, with the multilingual state-of-the-art model XLM-RoBERTa outperforming other algorithms using raw untranslated text. The combination of both traditional and deep learning algorithms allows for increased performance through the latter, while also gaining insight regarding tweet structure from the interpretability of the former. [ABSTRACT FROM AUTHOR]

Background: The misestimation of surgical risk is a serious threat to the lives of patients when implementing surgical risk calculator. Improving the accuracy of postoperative risk prediction has received much attention and many methods have been proposed to cope with this problem in the past decades. However, those linear approaches are inable to capture the non-linear interactions between risk factors, which have been proved to play an important role in the complex physiology of the human body, and thus may attenuate the performance of surgical risk calculators.Methods: In this paper, we presented a new surgical risk calculator based on a non-linear ensemble algorithm named Gradient Boosting Decision Tree (GBDT) model, and explored the corresponding pipeline to support it. In order to improve the practicability of our approach, we designed three different modes to deal with different data situations. Meanwhile, considering that one of the obstacles to clinical acceptance of surgical risk calculators was that the model was too complex to be used in practice, we reduced the number of input risk factors according to the importance of them in GBDT. In addition, we also built some baseline models and similar models to compare with our approach.Results: The data we used was three-year clinical data from Surgical Outcome Monitoring and Improvement Program (SOMIP) launched by the Hospital Authority of Hong Kong. In all experiments our approach shows excellent performance, among which the best result of area under curve (AUC), Hosmer-Lemeshow test ([Formula: see text]) and brier score (BS) can reach 0.902, 7.398 and 0.047 respectively. After feature reduction, the best result of AUC, [Formula: see text] and BS of our approach can still be maintained at 0.894, 7.638 and 0.060, respectively. In addition, we also performed multiple groups of comparative experiments. The results show that our approach has a stable advantage in each evaluation indicator.Conclusions: The experimental results demonstrate that NL-SRC can not only improve the accuracy of predicting the surgical risk of patients, but also effectively capture important risk factors and their interactions. Meanwhile, it also has excellent performance on the mixed data from multiple surgical fields. [ABSTRACT FROM AUTHOR]

Natural terrain landslides are mainly triggered by rainstorms in Hong Kong, which pose great threats to life and property. To mitigate landslide risk, building a prediction model which could provide information on both spatial and temporal probabilities of landslide occurrence is essential but challenging. In this paper, real-time rainfall conditions are incorporated into landslide prediction through a unique rainstorm-based database of reported landslides. Other landslide controlling factors related to topography, geology, and land cover are also considered. Five machine learning methods, including logistic regression, random forest, adaboost tree, support vector machine, and multilayer perceptron, are utilized and compared. Validated against historical rainstorms, the machine learning powered landslide prediction model could reasonably forecast the occurrence of landslides in a spatiotemporal context. Moreover, the effects of different rainstorm characteristics in terms of distinct rainfall spatial distribution and intensity on landslide susceptibility could also be captured by this model. For the landslide controlling factors investigated, rolling rainfall factors are proven to play a more important role than antecedent rainfall factors for landslide prediction. Among the five machine learning methods, the random forest model yields the most promising results in terms of all performance indicators (i.e., classification accuracy, recall, precision, area under curve, and overall accuracy). [ABSTRACT FROM AUTHOR]

Representing building layouts as graphs can extract critical design patterns that would facilitate space syntax analyses as well as design mining and automation but traditional approaches (e.g., non-weighted adjacent graphs) encountered problems in modular buildings, as they are largely shaped under the principle of 'modularity' rather than freeform cast in-situ elements. This paper attempts to develop a novel analytical tool called ModularGraph to represent modular building layouts (MBLs) as graphs considering their unique adjacency, connectivity, and conjoint relationships in a triumvirate. It does so by developing a prototype then applying it to 36 modular buildings for iteration, finetuning, and finalizing. It is found that ModularGraph can effectively translate heterogeneous forms of MBLs into unified graph-based representations with rich graphic and semantic information. This study not only contributes an innovative graph analytic tool for design pattern mining, but also lays a stepping stone towards generative AI for modular building design. • Propose a graph triumvirate structure for modular building layout representation. • Incorporate a weight mechanism to indicate the strength of spatial relationships for better semantic interpretations. • Perform a quantitative analysis on the layouts of 36 modular buildings in Hong Kong using the proposed framework. • Uncover patterns in modular building layout designs exemplified by ModularGraph. • Develop a machine learning model to learn knowledge from layout designs represented by ModularGraph. [ABSTRACT FROM AUTHOR]

Active school commuting makes a vital contribution to physical activity, thus improving the health and well-being for children and adolescents. The built environment is widely acknowledged as a factor that can affect travel behavior. However, few studies assess the influence of micro-level streetscape features on active school commuting using street view images. Additionally, existing research often presupposes that the relationship between built environment and walking to school is linear or generalized linear. Using data from the 2016 census and street view images in Hong Kong, this paper investigates the non-linear relationship between the micro-level streetscape features and propensity of walking to school with several machine learning methods. A refined evaluation of streetscape features is conducted through semantic segmentation, object detection and perceptual color analysis. The results show that the non-linear model facilitates a deeper understanding of the genuine relationship between streetscape features and propensity of walking to school. Grass view index, mean saturation of buildings and number of traffic lights have greater significance in predicting walking propensity. Moreover, some of the streetscape features have salient threshold effects, indicating that environmental interventions would only be effective within a specific range. These findings can provide nuanced and fine-grained guidance for building a walkable, children-friendly and sustainable city. • Machine learning techniques are applied to explore the non-linear relationship. • Streetscape features exhibit salient non-linear and threshold effects on walking propensity. • The hierarchy of priorities and most effective ranges for environmental intervention are revealed. [ABSTRACT FROM AUTHOR]

• Predicting departure delay in parallel may cause ambiguity in decision making. • A novel hierarchical integrated machine learning model helps to improve prediction. • The historical data provided by an international airline was used for the study. • The model is the best alternative to regression and multiclass classification. Flight delays may propagate through the entire aviation network and are becoming an important research topic. This paper proposes a novel hierarchical integrated machine learning model for predicting flight departure delays and duration in series rather than in parallel to avoid ambiguity in decision making. The paper analyses the proposed model using various machine learning algorithms in combination with different sampling techniques. The highly noisy, unbalanced, dispersed, and skewed historical high dimensional data provided by an international airline operating in Hong Kong was used to demonstrate the practical application of the model. The result shows that for a 4-h forecast horizon, a constructive neural network machine learning algorithm with the Synthetic Minority Over Sampling Technique-Tomek Links (SMOTETomek) sampling technique was able to achieve better average balanced recall accuracies of 65.5%, 61.5%, 59% for classifying delay status and predicting delay duration at thresholds of 60 min and 30 min, respectively. Similarly, for minority labels, the precision-recall and area under the curve showed that the proposed model achieved better results of 32.44% and 35.14% compared to the parallel model of 26.43% and 21.02% for thresholds of 60 min and 30 min, respectively. The effect of different sampling techniques, sampling approaches, and estimation mechanisms on prediction performance is also studied. [ABSTRACT FROM AUTHOR]

This paper describes the prediction of settlements induced by urban area tunneling using five machine learning (ML) algorithms. The settlement database, which was collected from a subway tunnel project in Hong Kong, consisted of 253 settlement measurements and 32 settlement influencing factors. The Bayesian optimization-based hyperparameter tuning was applied to efficiently explore optimal combinations and to enhance prediction performance. The optimal hyperparameters were selected by considering the three-fold cross-validation (CV) result of training data. The performance of the developed model was evaluated by comparing the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values. The extreme gradient boosting algorithm demonstrated the highest prediction accuracy with RMSE, MAE, and R2 values of 1.606, 1.331, and 0.835, respectively. • Tunneling-induced surface settlements predicted with five machine learning algorithms. • Bayesian optimization demonstrated excellent hyperparameter tuning search ability. • Prediction performance of trained models validated using three-fold cross-validation. • Consistency of prediction results validated with acceptable residual errors. • XGB model achieved the highest prediction performance with RMSE of 1.606. [ABSTRACT FROM AUTHOR]

[Display omitted] • The communal areas can have different levels of energy performance in the 15 types of public residential buildings under study. • Spatial design features can evidently affect the energy consumption in the communal areas. • The ability to harvest daylight serves as a key determinant of energy efficiency. • Both econometric and machine learning techniques are proved to be effective to generate reliable findings. In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed. • A novel artificial intelligence and object-based landslide susceptibility method is proposed and validated. • The first set of territory-wide landslide susceptibility maps for Hong Kong is produced based on AI techniques. • CNN-LSTM and BiLSTM-RNN are successfully applied to landslide susceptibility analysis for the first time. • The proposed AI-powered object-based method outperforms the cell-based method significantly. [ABSTRACT FROM AUTHOR]