Flow Estimation Research Articles

A Deep Learning Model for Precipitation Nowcasting Using Multiple Optical Flow Algorithms

Abstract The optical flow technique has advantages in motion tracking and has long been employed in precipitation nowcasting to track the motion of precipitation fields using ground radar datasets. However, the performance and forecast time scale of models based on optical flow are limited. Here, we present the results of the application of the deep learning method to optical flow estimation to extend its forecast time scale and enhance the performance of nowcasting. It is shown that a deep learning model can better capture both multispatial and multitemporal motions of precipitation events compared with traditional optical flow estimation methods. The model comprises two components: 1) a regression process based on multiple optical flow algorithms, which more accurately captures multispatial features compared with a single optical flow algorithm; and 2) a U-Net-based network that trains multitemporal features of precipitation movement. We evaluated the model performance with cases of precipitation in South Korea. In particular, the regression process minimizes errors by combining multiple optical flow algorithms with a gradient descent method and outperforms other models using only a single optical flow algorithm up to a 3-h lead time. Additionally, the U-Net plays a crucial role in capturing nonlinear motion that cannot be captured by a simple advection model through traditional optical flow estimation. Consequently, we suggest that the proposed optical flow estimation method with deep learning could play a significant role in improving the performance of current operational nowcasting models, which are based on traditional optical flow methods. Significance Statement The purpose of this study is to improve the accuracy of short-term rainfall prediction based on optical flow methods that have been employed for operational precipitation nowcasting. By utilizing open-source libraries, such as OpenCV, and commonly applied machine learning techniques, such as multiple linear regression and U-Net networks, we propose an accessible model for enhancing prediction accuracy. We expect that the improvement in prediction accuracy will significantly improve the practical application of operational precipitation nowcasting.

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

A Deep Graph Neural Network Approach for Assessing Origin Destination Traffic Flow Estimates Based on COVID-19 Data

A Deep Graph Neural Network Approach for Assessing Origin Destination Traffic Flow Estimates Based on COVID-19 Data

Hydrological models and datasets to support flood flow estimation at river-bridge intersections

A working group on the "Hydraulic Compatibility of Bridges" (sites.google.com/view/gii-ponti) has been recently established in the hydraulic engineering academic community aiming at reviewing methodologies, studying good practices, and suggesting guidelines for assessing the bridge hydraulic compatibility. In this initiative, a subgroup is focusing on the hydrological aspects as in Italy there are not standard procedures to estimate design flood values requested by the Italian technical standards of constructions (NTC 2018) over the whole country as, for example, in the UK where the Flood Estimation Handbook provides standard data and methods. Some models are available at the regional scale but are often based on not up-to-date data; moreover, while in large rivers flood discharge values, defined according to the European Flood Directive have been computed and mapped, in small river catchments they are not available.In this context, the unit of Politecnico di Torino working on hydrology is going to release some country-wide hydrological datasets, including all the available official information on discharge and rainfall extremes updated to recent years, that can be used as a common reference for bridge analyses. These include high-resolution maps of extreme precipitation statistics, a database of flood flow extremes recorded at more than 600 river sections, and a catalogue of river catchments characteristics (morphology, climate, land use, etc).Nation-wide datasets can support the application of standardized hydrological analyses, needed for flood hazard assessment, especially in small basins. Operational products will be made available within the PNRR RETURN project in order to be easily available and referable for any hydrological analysis in Italy.

Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements

Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements

Point Cloud Densification Based on Scene Flow Estimation and Kalman Refinement

Point Cloud Densification Based on Scene Flow Estimation and Kalman Refinement

Multi-Person Pose Tracking With Sparse Key-Point Flow Estimation and Hierarchical Graph Distance Minimization.

In this paper, we propose a novel framework for multi-person pose estimation and tracking on challenging scenarios. In view of occlusions and motion blurs which hinder the performance of pose tracking, we proposed to model humans as graphs and perform pose estimation and tracking by concentrating on the visible parts of human bodies which are informative about complete skeletons under incomplete observations. Specifically, the proposed framework involves three parts: (i) A Sparse Key-point Flow Estimating Module (SKFEM) and a Hierarchical Graph Distance Minimizing Module (HGMM) for estimating pixel-level and human-level motion, respectively; (ii) Pixel-level appearance consistency and human-level structural consistency are combined in measuring the visibility scores of body joints. The scores guide the pose estimator to predict complete skeletons by observing high-visibility parts, under the assumption that visible and invisible parts are inherently correlated in human part graphs. The pose estimator is iteratively fine-tuned to achieve this capability; (iii) Multiple historical frames are combined to benefit tracking which is implemented using HGMM. The proposed approach not only achieves state-of-the-art performance on PoseTrack datasets but also contributes to significant improvements in other tasks such as human-related anomaly detection.

Extreme Flow Estimation in Ungauged Basins: Case of Eastern Black Sea

Taşkınlar, dünya genelinde can ve mal kayıpları açısından incelendiğinde depremlerin ardından en yüksek kayba neden olan doğal afetlerdir. Taşkınları önlemek ve taşkınlardan korunmak için yapılan çalışmalarda kullanılan en temel veri akarsu debileridir. Akarsu debilerinden istatistiksel yöntemler aracılığıyla taşkın debileri hesaplanabilmektedir. Dünyanın farklı yerlerinde olduğu gibi ülkemizde de akım verisi açısından fakir sahalar bulunmaktadır. Bu sahalarda yer alan havzalara ait akımların tahmini farklı yöntemlerle yapılabilmektedir. Bu çalışmada bahsedilen yöntemlerden birisi olan L-momentler yöntemi ile Doğu Karadeniz Havzasında yer alan alt havzaların taşkın debilerinin hesaplanması amaçlanmıştır. Bu amaç doğrultusunda havzadaki akım gözlem istasyonlarına bölgesel analizler uygulanmış, en uygun bölgesel dağılım belirlenmiş ve bu dağılıma uygun yöntemlerle farklı tekrar periyotlarına sahip taşkın debileri hesaplanmıştır. Çalışmanın çıktıları hem hidrolojik hem de hidrodinamik çalışmalar için altlık oluşturmaktadır.

Regional Flood Frequency Analysis in the Doce River Basin and Coastal Regions in Espírito Santo State, Brazil – Evaluation of the Performance of L-Moments Approach

Objective: To evaluate the performance of the Index-Flood methods using L-Moments when analyzing the regional flood frequency. Theoretical benchmark: The maximum stream flows estimation is relevant for projects and reduction of flood impacts. However, hydrologic regionalization methods are usually employed due to the absence of maximum stream flow data. In this context, it is relevant to understand the methods performance of regional analysis available. Method: Regional flood frequency analisys in the Doce River basin and in basins located predominantly in the state of Espírito Santo by the Index-Flood methods, using L-Moments, direct multiple regression and Dimensionless Curve. Results and conclusion: The Index-Flood method using L-Moments presented better results than the other methods of regionalization evaluated. Changes in stream flow series and configuration of homogeneous regions did not alter significantly the results obtained with the Index-Flood method using L-Moments. Implications of research: Improvement of the process of regional analysis of flood frequency. Originality/value: Consistent analysis of the frequency of occurrence of floods, a recurrent socio-environmental problem aggravated in the context of climate change, constitutes a relevant advance for the management of water resources and has the potential to assist in the establishment of public policies aimed at territorial planning.

Dynamic probabilistic analytical modeling for estimating rainfall–runoff transformation rates in drylands

Abstract Precipitation-runoff changes used in the development of precipitation-runoff models depict general runoff mechanisms in physical precipitation-runoff processes. For this purpose, it must be able to adequately express the characteristics of the physical system. Similar to other water resources management models, analytical-probabilistic models may be developed with different levels of complexity according to different types of rainfall–runoff developments. In this research, a log-normal probabilistic model was used to estimate the return period of rainfall and Soil and Water Assessment Tool (SWAT) software was used to convert rainfall values into discharge and runoff height in agricultural lands. The slope and the level of plant shade cover were considered the two basic factors in the estimation of the subsurface flow used in agriculture. Slopes of less than 0.015 and land cover level of more than 70% of extreme points were obtained to ensure subsurface water sources and prevent erosion.

Effect of Teleconnection Patterns on the Formation of Potential Ecological Flow Variables in Lowland Rivers

Climate is probably the most important factor affecting river discharge and flow dynamics. Low flows in rivers during the warm period cause stress to aquatic ecosystems and pose a challenge to sustainable water management. Previous research has shown that the average minimum discharge of the 30 driest continuous days, known as Q30, is a suitable measure for ecological flow estimation in Lithuania. This study aims to examine whether large-scale atmospheric processes, so-called teleconnections, can have an impact on Q30 during the warm period. Hydrological data for 1961–2020 from 25 water gauging stations were used to search for hydrological response signals with five selected climate indices (NAO, SCA, POL, EA/WR, and EA). Pearson correlation and Wilcoxon–Mann–Whitney test approaches were applied. The results suggested that the EA/WR and NAO had the strongest influence on Q30 in the studied region during the warm period. The positive phases of the indices tended to cause a greater decrease in Q30 values due to the prevailing easterly edge of the anticyclonic circulation over the studied region determined by the EA/WR and NAO indices, while the negative phases of the mentioned indices caused an increase and greater dispersion of Q30.

Ecological environmental flow estimation for rivers with complicated hydraulic conditions.

Estimating ecological environmental flow in tidal rivers is one of the major challenges for sustainable water resource management in estuaries and river basins. This paper presents an ecological environmental flow framework that was developed to accommodate highly dynamic medium tidal estuaries found along the Yellow Sea coast of China. The framework not only proposes a method of water quality-based ecological flow for tidal gate-controlled rivers but also proposes a method of water demand for scouring and silting to protect ports in coastal viscous sediment environments. The framework integrates the instream water requirements of water quality, sediment and basic ecological flow, and considers the temporal and spatial variation differences for the environmental flow requirements of tidal rivers. This study emphasizes the significance and necessity of continuous monitoring of ecological data in determining the environmental flow of tidal rivers. The output of this study could provide vital references for decision-making and management of the water resource allocation and ecological protection in tidal rivers.

Calcite fabric development in calc-mylonite during progressive shallowing of a shear zone: An example from the South Tibetan Detachment system (Kali Gandaki valley, Central Himalaya)

Calcite-rich lithologies within the Annapurna Detachment zone, a strand of the South Tibetan Detachment System in central Himalaya (Kali Gandaki region, Western Nepal) have been characterized for the superimposition of microstructures to unravel deformation variations during syn-collisional exhumation. Finite-strain, grain size, twinning and crystallographic preferred orientations have been combined to define the contribution of dynamic recrystallization and twinning in calcite in the overall deformation. Dynamic recrystallization and twinning occurred respectively at temperatures of c. 400–550 °C and <250 °C. The comparison of calcite-based paleopiezometers indicates that cooling occurred along with an increase in differential stress from c. 4–19 MPa up to c. 118–154 MPa at decreasing strain rates. Flow estimates with the subsimple shear (30–50% of simple shear) regime for both fabrics support a single progressive deformation where the plastic regime progressively changed. We interpret the changes in intracrystalline deformation mechanisms in calcite and their differences in differential stress records, deformation temperature, and strain rate as linked to the exhumation of the rocks induced by the detachment, from deeper to upper crustal levels. Results for the Annapurna Detachment zone have been compared with the literature database for the regional continuation along the Himalaya, the South Tibetan Detachment System, which is defined by a lower main shear zone and an upper brittle fault. Data comparison led us to link our early stage of ductile shearing to that elsewhere recorded in the main lower branch of the system. Conversely, data for the late shearing are not found for the main branch when affecting quartz-bearing rocks, as strain hardening resulted in an upward partitioning of the deformation and in the localization of a brittle fault. Therefore, this study highlights how variations in lithology in regional-scale shear zones influences the exhumation path and the overall architecture of shear zones.

A fusion model of temporal graph attention network and machine learning for inferring commuting flow from human activity intensity dynamics

Accurately estimating commuting flow is essential for optimizing urban planning and traffic design. The latest graph neural network (GNN) model with the encoder-decoder-predictor components has several limitations. First, it ignores the temporal dependency of node features for node embedding. Second, different estimation methods used in the decoder and predictor make it difficult to distinguish the contribution of node embedding or estimation method to flow estimation. Third, finer-grained socio-economic features of nodes are difficult to obtain due to low data availability. To address these problems, this study proposes a fusion model of temporal graph attention network and machine learning (TGAT-ML) to infer commuting flow from dynamic human activity intensity distribution. The model first constructs a commuting network with temporal human activity intensity as node features. A temporal graph attention network is then developed to capture the spatiotemporal dependency. The learned node embedding is generated by using a machine learning method in the decoder. Finally, based on learned node embedding and machine learning method used in the decoder, the commuting flow intensity is estimated. Results from an empirical study using the Baidu heat map data of Guangzhou city indicate that the proposed fusion model TGAT-ML outperforms all other baseline models. This study proves that the model performance can be significantly enhanced by determining the edge existence through commuting time-based approach, integrating temporal convolution with graph convolution, and unifying flow estimation method in both decoder and predictor. This work enables commuting flow estimation from dynamic human activity intensity and broadens existing flow generation research in terms of data and methodology.

Joint estimation of pose, depth, and optical flow with a competition–cooperation transformer network

Estimating depth, ego-motion, and optical flow from consecutive frames is a critical task in robot navigation and has received significant attention in recent years. In this study, we propose PDF-Former, an unsupervised joint estimation network comprising a full transformer-based framework, as well as a competition and cooperation mechanism. The transformer framework captures global feature dependencies and is customized for different task types, thereby improving the performance of sequential tasks. The competition and cooperation mechanisms enable the network to obtain additional supervisory information at different training stages. Specifically, the competition mechanism is implemented early in training to achieve iterative optimization of 6 DOF poses (rotation and translation information from the target image to the two reference images), the depth of target image, and optical flow (from the target image to the two reference images) estimation in a competitive manner. In contrast, the cooperation mechanism is implemented later in training to facilitate the transmission of results among the three networks and mutually optimize the estimation results. We conducted experiments on the KITTI dataset, and the results indicate that PDF-Former has significant potential to enhance the accuracy and robustness of sequential tasks in robot navigation.

An optical flow estimation method based on multiscale anisotropic convolution

An optical flow estimation method based on multiscale anisotropic convolution

Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project – ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodologyapplied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.

Real-Time Optical Flow Estimation Method Based on Cross-Stage Network

In this paper, a real-time optical flow estimation method based on a cross-stage network is proposed. The proposed model is designed with a network structure with encoders and decoders. The proposed method combines cross-stage network technology with the network structure of FlowNet2 and RAFT to achieve improved parameter number and estimation performance. For real-time optical flow estimation, it is important to maintain performance while reducing the number of parameters in the network. In the proposed method, structural convergence is performed to increase performance while reducing the number of parameters by applying the cross-stage network structure. The proposed model is designed to solve the bottlenecks in model accuracy and complexity by separating feature extraction and flow estimation processes. Flying Chairs, Flying Things 3D, and KITTI datasets were used to evaluate the performance of the proposed model, and the experimental results show superior performance compared to previous traditional methods.

Bayesian estimation of glacier surface elevation changes from DEMs

Accurate estimates of glacier surface elevation changes are paramount for various aspects of the study of the cryosphere, from glacier flow and thickness estimates to hydrological forecasts and projections of sea-level-rise. We present a novel probabilistic framework to filter outliers and estimate uncertainties in glacier surface elevation changes computed from the subtraction of digital elevation models (DEM). Our methodology frames outlier filtering as a Bayesian inference problem, thus characterizing the state of knowledge on glacier surface elevation changes through the posterior distribution as the combination of glacier volume variation observations and prior knowledge arising from previously collected data and/or modeled results. We validate this technique with experiments using Gaussian random fields to generate artificial noise in glacier surface elevation variation observations and show that the model satisfactorily culls the simulated outliers. Surface elevation change estimates are consistent with results computed from widely-used outlier filtering and uncertainty estimation techniques. The Bayesian framework allows unifying DEM error models with physical considerations on glacier surface elevation changes within a simple, statistically coherent model preventing temporal correlation and additional biases in other techniques. On the basis of these results, we discuss the implications of DEM uncertainty and offer suggestions for the glaciological community.

Dual-Channel Semi-Supervised Adversarial Network for Building Segmentation from UAV-Captured Images

Accurate building extraction holds paramount importance in various applications such as urbanization rate calculations, urban planning, and resource allocation. In response to the escalating demand for precise low-altitude unmanned aerial vehicle (UAV) building segmentation in intricate scenarios, this study introduces a semi-supervised methodology to alleviate the labor-intensive process of procuring pixel-level annotations. Within the framework of adversarial networks, we employ a dual-channel parallel generator strategy that amalgamates the morphology-driven optical flow estimation channel with an enhanced multilayer sensing Deeplabv3+ module. This approach aims to comprehensively capture both the morphological attributes and textural intricacies of buildings while mitigating the dependency on annotated data. To further enhance the network’s capability to discern building features, we introduce an adaptive attention mechanism via a feature fusion module. Additionally, we implement a composite loss function to augment the model’s sensitivity to building structures. Across two distinct low-altitude UAV datasets within the domain of UAV-based building segmentation, our proposed method achieves average mean pixel intersection-over-union (mIoU) ratios of 82.69% and 79.37%, respectively, with unlabeled data constituting 70% of the overall dataset. These outcomes signify noteworthy advancements compared with contemporaneous networks, underscoring the robustness of our approach in tackling intricate building segmentation challenges in the domain of UAV-based architectural analysis.