Time Series Research Articles

Detecting And Predicting Price Jumps With Consecutive Signature Distance

In order to estimate the hidden states of dynamical systems, Jump Models cluster financial observations along time series and impose a cost on jumping from one cluster to another. While these models can detect abrupt changes in the underlying time series, they suffer from two major drawbacks when it comes to detecting price jumps: (1) they cannot detect two (or more) consecutive jumps; (2) they cannot dissociate high volatility from jumps. To remedy these drawbacks, Bloch and Liao consider two seemingly unrelated problems: (1) Detecting and predicting price jumps by identifying whether a new observation results in a price jump relative to previous observations; (2) Anomaly detection of segments of a time series, that is, fixed size segments of a time series are treated as a normal corpus, and search for outliers. It is observed that while these problems are clearly different, the former can be reformulated in terms of the latter and they can therefore associate jump indicators to data-driven metrics for anomaly detection. Bloch and Liao propose a three-step process: (1) jump detection: the variance norm is combined with path signatures to come up with a data-driven jump indicator; (2) training: Bloch and Liao use this Variance Norm Jump Indicator as an input feature for Jump Models; (3) prediction: the authors use new incoming data to predict its hidden state. This approach aims at enhancing the model�s accuracy and predictive capabilities by leveraging the strengths of variance norm on detecting data points after the jump as outliers from previous distribution. Bloch and Liao conducted an extensive analysis on simulated data, examining the structure, benefits and limitations of the approach, and found that they could retrieve the true hidden states with high accuracy without using future information.

Enhanced seed yield of full-season soybean when rotated with cereals and cover crops as compared to monoculture in a long-term experiment

Soybeans are of great importance to the global economy, but the cultivation as monoculture has shown several negative environmental implications in the long-term. Long-term studies demonstrate the cumulative effects of rotations on soil variables, but few studies have considered changes during consecutive years in a time series of soybean as a monoculture. The inclusion of cereals and cover crops in rotation with soybean increases the intensification sequence index (ISI, time with crops actively growing during the year) and increases in soybean yield are expected. In line with this, some studies suggest that only including a winter cover crop will increase ISI and raise production. However, these comparisons have not been made in the long term. The objectives of this work were to: i) compare the evolution of full-season soybean yield and production when sowed as monoculture and in cropping sequences that include cereal crops and cover crops, and ii) quantify a yield gap in full-season soybean due to monoculture by evaluating the long-term residual effect of crop sequences with different land occupation. Soybean yield time series in a 14-years period in rotation or as monoculture were studied in a long-term field experiment under no-tillage established in 2006 in the Northern Pampas region of Argentina. Rotations consisted in sequences that included soybean (S), maize (M) and wheat (W), and also incorporated wheat as a winter cover crop (CC): S-S, CC/S-CC/S, S-W/S-M, CC/S-W/S-M, M-W/S, and CC/M-W/S, with ISI values of 0.39, 0.69, 0.55, 0.64, 0.65 and 0.80, respectively. In 2021/22 season, full-season soybean was planted in all plots as a "test" crop, to evaluate the long-term residual effect of sequences with different intensification indexes. Seed yield averaged 3249 kg ha−1 among years. During the 14 years under study, seed yield in S-W/S-M surpassed S-S by 28 % in 6 out of 14 years. The differences between S-S with respect to this sequence were observed consistently from the ninth crop season (i.e. since 2017/18). The inclusion of a cover crop within a soybean monoculture (i.e. CC/S-CC/S) showed similar seed yields as in S-S in 11 out of 14 years. The analysis of the test crop showed yield-gaps when soybeans were grown as monoculture (seed yield in rotations minus seed yield in S-S) of 9, 20, 27, 21 and 31 % for CC/S-CC/S, S-W/S-M, CC/S-W/S-M, M-W/S and CC/M-W/S, respectively. In summary, soybean in rotation with cereals showed an average increase of ca. 346 kg ha−1 and including a cover crop within a soybean monoculture did not increase seed yield at the levels observed when rotated with cereals. Finally, when estimating seed yield-gaps in soybean, it is important to set in which rotation soybean is placed, since a higher maximum yield is expected to happen when soybean follows a more intensified rotation.

Identify and map coastal aquaculture ponds and their drainage and impoundment dynamics

Sustainable management of coastal aquaculture ponds could achieve win-win between food and economic benefits and ecological conservation including waterbird. In this study, 5,790 Harmonized Landsat and Sentinel-2 images from July 2021 to June 2022 and 498 Sentinel-1 images from July 2021, August 2021, and June 2022 as supplementary data were collected to calculate multiple water indices. Based on Otsu algorithm to distinguish between water and non-water region and Savitzky-Golay filtering to optimize time series, coastal aquaculture ponds were identified using the SNIC. Furthermore, their drainage and impoundment phases were determined using the Dynamic Time Warping-Kmeans++ method. Finally, a new 30-m resolution dataset at the national scale of China was generated with an overall accuracy greater than 90 % for both the pond map and the drainage and impoundment phases. Our observations revealed that the total area was 7,919.53 km2, with the largest pond area in Shandong Province. Among the coastal aquaculture ponds, 27.95 % were seasonal aquaculture ponds, 70.32 % were yearlong aquaculture ponds, and 1.49 % were abandoned aquaculture ponds. Drainage start dates, end dates, and durations were calculated based on abrupt changes in the water proportion time series. Drainage start dates were concentrated from September to December, while drainage end dates were from January to April. Drainage durations of coastal aquaculture ponds ranged from two weeks to six months, with Shanghai Municipality having the longest drainage durations and Taiwan Province having the shortest drainage durations. The findings could provide scientific support for modifying the drainage and impoundment phases of coastal aquaculture ponds to achieve the win–win goal of improving economic development and protecting waterbirds or improving offshore water quality.

An Unsupervised Learning framework for health diagnosis by incorporating Multiscale Data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

Has unsustainable groundwater use induced low flow regimes in the Urucuia Aquifer System? An urgent call for integrated water management

Groundwater depletion in the Urucuia Aquifer System (UAS) has concerned the Brazilian water agencies since it is the principal water source in the arid length of the São Francisco river. In contrast, the irrigation fields increasing and uncertainty regarding climate variability have impaired the management of this important hydrograph region of Brazil. Therefore, the proper management of the UAS relies on the correct definition of the factors that are impacting most of the ground and surface waters, i.e., whether they are from climate or anthropogenic activities. Relying on Artificial Intelligence (AI) modeling and hydrological signatures, this study proposed a methodology to disentangle the role of climatic variability and groundwater pumping on streamflow dynamics by rebuilding natural time series in four watersheds within the UAS’s boundaries. By comparing the natural to the observed streamflow time series, the long-term 90th percentile (Q_90) discharge, from which 80% can be granted for multiple uses in the State of Bahia, decreased by up to 50% in the UAS. Yet, the watersheds’ productivity, given by the 90% specific yield, ranged from -52.3% (3.8 L/s.km2 to 1.8 L/s.km2) to -74.0% (23.4 L/s.km2 to 6.1 L/s.km2). Low flow duration was ∼ 1,650 days in natural conditions and increased to 10,354 days in the current land use and cover scenario. Changes in the maximum low flow duration length ranged from 191.7% to 1,315.7%, and in the low flow, deficits were up to 7,150%. These results highlight that groundwater pumping is the principal factor of the UAS’s dryness since climate variability could not track streamflow decreases. However, climate variability is secondary because of the intensification of the atmosphere demand. Although it is a Brazilian application, the proposed methodology can be applied in other aquifer systems to guide decision-makers' management strategies worldwide.

Petroleum evolution and its genetic relationship with the associated Jinding Pb[sbnd]Zn deposit in Lanping Basin, Southwest China

The spatial association of hydrocarbons with metalliferous ore deposits is found worldwide and is particularly common to MVT PbZn deposits. Heavy oil and bitumen are found in the Jinding PbZn deposit within the Lanping Basin, South China. However, the temporal and genetic associations between hydrocarbons and the deposit are still controversial. To this end, integrating Raman analysis, ReOs geochronology and transmission electron microscopy analysis of the bitumen and in situ S isotope analyses of the sulfide, the petroleum evolution of the Jinding reservoir and its genetic relationship with the PbZn deposits were discussed. Bitumen ReOs data from this study and published works indicate that the late Triassic shales underwent two distinct oil-generation events before mineralisation (~25 Ma), with initial oil generation occurring during the early Cretaceous (~116 Ma) and the second during the early Paleogene (ca. 68–59 Ma). These two ages agree with the modelled thermal history of the Jinding reservoir. Combining the oil-before-ore timing sequence, high metal abundance of the bitumen, two negative sulfur isotope peaks of the sulfide and high S/C atomic ratio of the bitumen from the Jinding deposit, the oil-containing aqueous solutions were considered as one metal carrier during the hydrocarbon migration and accumulation; further, bacterial sulfate reduction and thermo-chemically induced sulfate reduction processes could have participated in the supply of reduced sulfur for the PbZn deposit precipitation.

The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

The Capacity and Robustness Trade-Off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

Laplacian Convolutional Representation for Traffic Time Series Imputation

Laplacian Convolutional Representation for Traffic Time Series Imputation

Enhancing water quality prediction with advanced machine learning techniques: An extreme gradient boosting model based on long short-term memory and autoencoder

Achieving carbon neutrality in the pulp and paper industry necessitates effectively recycling pulp and papermaking wastewater, where continuous monitoring of effluent quality indices is crucial. This study suggests a novel machine learning-based model named LSTMAE-XGBOOST that integrates the feature extraction capabilities of autoencoder, the sequential feature learning capabilities of LSTM, and the high prediction accuracy of XGBOOST. This model is capable of extracting the complex relationships, non-Gaussian characteristics, and time series features from the papermaking wastewater data, and it demonstrates superior predictive performance. Compared to traditional machine learning models, the proposed model exhibits higher prediction accuracy. Specifically, when contrasted with partial least squares regression, LSTMAE-XGBOOST achieves a 40% increase in R2 and a 35% reduction in RMSE. Further comparative assessments against other machine learning-based hybrid models with similar structures confirm the superiority of integrating LSTM and XGBOOST within the hybrid model approach. This study contributes a compelling methodology for modeling effluent quality indices, offering significant implications for environmental management in the pulp and paper industry.

Modeling the health impact of wastewater contamination events in drinking water networks

Pathogen intrusion in drinking water systems can pose severe health risks. To better prepare in planning and responding to such events, computational models that capture the intrusion and health impact dynamics are needed. This study presents a novel benchmark testbed that integrates current knowledge on pathogen transport and fate in chlorinated systems and can assess infection risk from contamination events. The model considers organic matter degradation, chlorine decay mechanisms, pathogen inactivation kinetics, as well as stochastic water demands.We studied modeling of wastewater intrusion events that can occur anywhere within a chlorinated and non-chlorinated network. We applied the Quantitative Microbial Risk Assessment framework focusing on three pathogens: enterovirus, Campylobacter, and Cryptosporidium, and their respective dose-response models. Synthetic household-level water demand time series were used to model the individual water consumption timing and calculate the infection risk (exposure via ingestion).Model outcomes indicate that while chlorination aids mitigation, larger contaminations can still lead to infections due to chlorine resistance (for Cryptosporidium) and chlorine depletion at the contamination point. In our example scenarios, chlorine-susceptible pathogens infected of the downstream population, while chlorine-resistant ones infected the entire downstream population. Enterovirus infection risk is higher, despite the concentrations in the contamination source being lower, due to the lower susceptibility to chlorine than Campylobacter. In non-chlorinated networks, the modeled wastewater contamination events led to infection risk in the total population, depending on the contamination location. Hydraulic uncertainty had a limited influence on infection risk. Furthermore, Campylobacter’s infection risk is more sensitive to the initial concentration in the contamination source whereas enterovirus infection risk to the inactivation rate. The model further indicates that the time window for effective mitigation of the magnitude of a waterborne outbreak is short (within hours).

Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis

BackgroundUttar Pradesh, India’s largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the NCAP’s impact on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. MethodsMonthly data from 2016 to 2023 on AQI and PM10 concentrations were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM10 levels from 2017 to 2023 were evaluated using the Friedman Test. Prophet models forecasted PM10 concentrations for 2025-26, with relative percentage changes calculated and model evaluation metrics assessed. FindingsMost cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72·73) in 2023, classified as 'moderate' by WHO standards. Gorakhpur consistently showed 'poor' AQI levels, peaking at 249·31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM10 levels by 50% and 32%, respectively. Moradabad’s model showed the best performance with an R2 of 0·81, MAE of 17·27 μg/m3, and MAPE of 0·10. InterpretationForecasting PM10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. FundingNo funding was issued for this research.

A multiple behaviour temporal network analysis for health behaviours during COVID-19.

The aim of this study was to examine the temporal dynamics of multiple health behaviours (physical activity, alcohol consumption, healthy eating, cigarette consumption, recreational drug use, vaping), and pandemic-related health behaviours (e.g., hand washing, physical distancing) using network psychometrics. The International COVID-19 Awareness and Responses Evaluation (iCARE) study is an international multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. A sub-sample of longitudinal data from Canadians (n = 254) was analysed across four waves (February-July 2020). We used temporal network models to fit temporal networks, contemporaneous networks, and between-subject networks from items within the iCARE survey. Positive temporal associations were observed between physical activity and healthy eating, and a bidirectional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed positive associations between consumption behaviours (vaping, cigarette use, alcohol use, and recreational drug use), and negative associations between physical activity and drug use, and healthy eating and cigarette use. Health behaviours are interconnected and can be modelled as networks or behavioural systems. The application of temporal network analysis to the study of multiple health behaviours is well suited to address key research questions in the field such as 'how do multiple health behaviours co-vary with one another over time'. Future research using time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis-generating insights.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.

A hybrid model based on complementary ensemble empirical mode decomposition, sample entropy and long short-term memory neural network for the prediction of time series signals in NPPs

Accurate and reliable predictions are fundamental for the condition monitoring and maintenance of components and systems in nuclear power plants (NPPs). In this work, we propose a hybrid condition prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SampEn) and optimized long short-term memory (LSTM) neural network. Firstly, the CEEMD decomposes the time series signals into multiple subsequences called intrinsic mode functions (IMFs), by doing so, the complexity of the time series signals can be reduced, and this facilitates the accurate prediction of the original signals. Then, in order to reduce the calculation cost of prediction models for subsequences, SampEn is used to measure the complexities of the IMFs, and the IMFs whose values of SampEn are lower than the average are aggregated into a new component. Finally, the LSTM with the hyperparameters optimized by the Bayesian optimization algorithm (BOA) is used to perform the prediction of each component. The prediction results of the original signals are reconstructed by synthesizing the predictions of all components. The proposed hybrid prediction model is utilized on the time series signals collected from an NPP. The results obtained show that the proposed approach can capture the characteristics of the signals and has better performance in prediction accuracy than other models.

Short-term inbound passenger flow prediction at high-speed railway stations considering the departure passenger arrival pattern

Accurate prediction of short-term inbound passenger flow at high-speed railway stations is of great significance for the refined operation of stations, the formulation of emergency plans, and the provision of intelligent services. The arrival of passengers traveling on the same train at the same station shows a similar pattern, which is called the departure passenger arrival pattern (DPAP). The short-term inbound passenger flow at the station is composed of the short-term inbound passenger flow of all waiting trains within the same period. Inspired by this, this paper develops an ensemble prediction model based on the time series decomposition modeling strategy to introduce the DPAP to the short-term inbound passenger flow prediction at stations. Firstly, we propose a new framework for studying the DPAP to calculate the fitted station short-term inbound passenger flow, which is only affected by the DPAP. During this process, we find that 7minutes is the optimal time granularity. Secondly, based on the singular spectrum analysis, we prove that the DPAP is the determining factor affecting the station short-term inbound passenger flow. Finally, we propose an ensemble prediction model that considers the DPAP to achieve short-term inbound passenger flow prediction at stations. The model consists of two parts: the deterministic and stochastic components prediction, where the former is predicted by the fitted station short-term inbound passenger flow, and the latter is achieved by the combination of historical stochastic components and weather type with the help of the Seq2Seq model based on time attention mechanism. Using real inbound passenger flow data, we compare the proposed model with 13 benchmark models and the results show that under different training and prediction steps, our model achieves optimal prediction performance, whether in all-day period and the busiest period of the station. Through further ablation experiments, it has been proven that the introduction of the DPAP effectively improves the prediction accuracy. Our model can provide scientific support for the intelligent operation of stations and the refined management of passenger flow.

A Model-Chain to Generate Q-/V-Band Attenuation Time Series From Short-Term Numerical Weather Predictions at Continental Scale

A Model-Chain to Generate Q-/V-Band Attenuation Time Series From Short-Term Numerical Weather Predictions at Continental Scale

The impact of sodium glucose co-transporter 2 inhibitors and glucagon-like peptide 1 receptor agonists on insulin utilisation and costs in Australia: a national retrospective observational cross-sectional study

Global insulin requirements for type 2 diabetes were predicted to increase by more than 20% from 2018 to 2030. However, this did not anticipate the rapid increase in use of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors that has occurred over recent years. The current study aims to examine changes in insulin utilisation and costs in Australia from 2003 to2023. We conducted a large-scale observational study of national insulin utilisation and expenditure in Australia from 2003 to 2023 using the Australian Pharmaceutical Benefits Scheme. The proportion of insulin-treated people with type 2 diabetes between 2013 and 2023 was estimated using National Diabetes Services Scheme data. Joinpoint models and interrupted time series analysis were used to examine utilisation trends. Insulin utilisation (units of insulin per person with diabetes) increased by an average of 2.71% per annum (95% CI 1.97, 3.73) from 2003 to 2015, then fell by 2.70% per annum (95% CI-4.55,-1.39) from 2015 to 2023. The proportion of insulin-treated people with type 2 diabetes increased by 1.00% per annum (95% CI 0.81, 1.25) from 2013 to 2020, then fell by 0.66% per annum (95% CI-1.62,-0.04) from 2020 to 2023. A 43% reduction in inflation-adjusted insulin expenditure was observed between 2015 and 2023 due to a combination of reduced utilisation and reduction in the price of insulin glargine. Projected global insulin requirements and costs may be less than previously anticipated if reduced use of insulin in Australia is similarly observed in other countries. No funding was received for this study.

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

Dynamic mode decomposition of GRACE satellite data

Advancements in satellite technology yield environmental data with ever improving spatial coverage and temporal resolution. This necessitates the development of techniques to discern actionable information from large amounts of such data. We explore the potential of dynamic mode decomposition (DMD) to discover the dynamics of spatially correlated structures present in global-scale data, specifically in observations of total water storage anomalies provided by GRACE satellite missions. Our results demonstrate that DMD enables data compression and extrapolation from a reduced set of dominant spatiotemporal structures. The accuracy of its predictions of global system dynamics is preserved in its reconstruction of local time series. These findings suggest potential uses of DMD in analysis of remote-sensing data for hydrologic applications.

Motion process and failure mode of Pusa landslide, China based on dense optical flow method

ABSTRACT Unmanned aerial vehicle (UAV) is widely applied in geohazard monitoring with its high resolution and feasibility features, where UAV videos can record the whole landslide failure process. Previous studies can only provide the qualitative depiction of the landslide failure process, rather than the quantitative motion process, which makes it difficult to analyse the landslide dynamic characteristics and failure mode. In this letter, we take UAV video of Pusa landslide, China obtained on 28 August 2017 to retrieve the landslide motion process by using the dense optical flow method. Firstly, the landslide boundary can be clearly depicted by the cumulative landslide motion time series maps. Secondly, the maximum cumulative motion distance of the Pusa landslide was located in the middle of the slope body, with the maximum distance as 79 m, while the maximum instantaneous motion rate was around 42 m/s in the middle and lower part of the slope body. Finally, the failure mode of Pusa landslide can be characterized as ‘fissure zone in the middle, fissure extension to the top, rock bulges outward in the middle and landslide failure to high-speed runout debris flow’.