U-FLOOD – topographic deep learning for predicting urban pluvial flood water depth

Glufosinate at 1.1 and 2.2 kg/ha injured ‘Koshihikari’ rice lines that were transformed with the BAR gene from 0 to 53%. However, transgenic ‘Gulfmont’ rice was not injured. Rice yields of transgenic ‘Gulfmont’ lines and six of nine ‘Koshihikari’ lines were not affected by 2.2 kg/ha glufosinate. In field studies, flooding reduced the efficacy of glufosinate in controlling red rice, and greenhouse tests determined that glufosinate efficacy was reduced when red rice was submerged between 25 and 50% of its height. Plant heights and dry weights of red rice increased as flood water depth increased at all rates of glufosinate.

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Water deep mapping from HJ-1B satellite data by a deep network model in the sea area of Pearl River Estuary, China

Open Geosciences ◽

10.1515/geo-2020-0267 ◽

2021 ◽

Vol 13 (1) ◽

pp. 782-795

Author(s):

Xiaoning Zhao ◽

Daqing Wang ◽

Haoli Xu ◽

Yue Shi ◽

Zhengdong Deng ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Model ◽

Water Depth ◽

Neural Network Model ◽

Rbf Neural Network ◽

River Estuary ◽

Pearl River Estuary ◽

Pearl River ◽

Deep Learning Model

Abstract Remote sensing (RS) water depth inversion is an important technology and the method of water depth measurement. Taking the waters around the islands outside the Pearl River Estuary as an example, five optical RS depth inversion algorithms were introduced. Then, five water depth inversion models were trained through the HJ-1B satellite RS image and the measured water depth data. The results show that the mean absolute error (MAE) of the deep learning model was the smallest (2.350 m), and that the distribution of predicted water depth points was closest to the actual value. Deep learning has been widely used in RS image classification and recognition and shows its advantages. Therefore, the deep learning model was applied to extract the depth of the shallow water. Meanwhile, the obtained inversion effect map is closest to the actual contour map. The water depth inversion performance of back propagation neural network model is better than that of the radial basis function (RBF) neural network model. Besides, the inversion accuracy of the RBF neural network may be affected due to the small amount of data and the improper number of hidden neurons. The results show broad application prospects of machine learning algorithms in RS water depth inversion. Also, this study provided data support for model optimization, training, and parameter setting.

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Shallow Water Bathymetry Mapping of Xinji Island Based on Multispectral Satellite Image using Deep Learning

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-020-01255-9 ◽

2021 ◽

Author(s):

Jiaxin Wan ◽

Yi Ma

Keyword(s):

Deep Learning ◽

Shallow Water ◽

Water Depth ◽

Satellite Image ◽

Absolute Error ◽

Coastal Zones ◽

Multispectral Data ◽

Novel Approach ◽

Multispectral Satellite Image ◽

Remote Sensing Techniques

AbstractNearshore bathymetry is a basic parameter of the ocean, which is crucial to the research and management of coastal zones. Previous studies have demonstrated that remote sensing techniques can be employed in estimating bathymetric information. In this paper, we propose a deep belief network with data perturbation (DBN-DP) algorithm for shallow water depth inversion from high resolution multispectral data, and applying it in Xinji Island of Malacca Strait and Yongxing Island in China. Results show that the DBN-DP method can produce more accurate water depth estimations than other traditional methods particularly for deeper water, which reaches 1.2 m of mean absolute error (MAE) and 12.8% of mean relative error (MRE) in Xinji Island. Most of the estimated bathymetry meet the category of zone of confidence C level defined by the International Hydrographic Organization. These findings are encouraging for employing deep learning in bathymetry, which may become a novel approach for bathymetric inversion in the future.

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A Physics-Informed, Machine Learning Emulator of a 2D Surface Water Model: What Temporal Networks and Simulation-Based Inference Can Help Us Learn about Hydrologic Processes

Water ◽

10.3390/w13243633 ◽

2021 ◽

Vol 13 (24) ◽

pp. 3633

Author(s):

Reed M. Maxwell ◽

Laura E. Condon ◽

Peter Melchior

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Water Depth ◽

Current Model ◽

Time Dependent ◽

Water Model ◽

Learning Approaches ◽

Simulation Based ◽

3D Cnn ◽

Learning Architectures

While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation.

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Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

Remote Sensing ◽

10.3390/rs12244068 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4068

Author(s):

Zihao Leng ◽

Jie Zhang ◽

Yi Ma ◽

Jingyu Zhang

Keyword(s):

Deep Learning ◽

Water Depth ◽

Learning Model ◽

Water Area ◽

Accuracy Evaluation ◽

Underwater Topography ◽

Wide Range ◽

Log Linear ◽

Deep Learning Model ◽

New Framework

The Liaodong Shoal in the east of the Bohai Sea has obvious water depth variation. The clear shallow water area and deep turbid area coexist, which is characterized by complex submarine topography. The traditional semi-theoretical and semi-empirical models are often difficult to provide optimal inversion results. In this paper, based on the traditional principle of water depth inversion in shallow areas, a new framework is proposed in combination with the deep turbid sea area. This new framework extends the application of traditional optical water depth inversion methods, can meet the needs of the depth inversion work in the composite sea environment. Moreover, the gate recurrent unit (GRU) deep-learning model is introduced to approximate the unified inversion model by numerical calculation. In this paper, based on the above-mentioned inversion framework, the water depth inversion work is processed by using the wide range images of GF-1 satellite, then the relevant analysis and accuracy evaluation are carried out. The results show that: (1) for the overall water depth inversion, the determination coefficient R2 is higher than 0.9 and the MRE is lower than 20% are obtained, and the evaluation index shows that the GRU model can better retrieve the underwater topography of this region. (2) Compared with the traditional log-linear model, Stumpf model, and multi-layer feedforward neural network, the GRU model was significantly improved in various evaluation indices. (3) The model has the best inversion performance in the 24–32 m-depth section, with a MRE of about 4% and a MAE of about 1.42 m, which is more suitable for the inversion work in the comparative section area. (4) The inversion diagram indicates that this model can well reflect the regional seabed characteristics of multiple radial sand ridges, and the overall inversion result is excellent and practical.

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Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030144 ◽

2021 ◽

Vol 10 (3) ◽

pp. 144

Author(s):

Asmamaw A Gebrehiwot ◽

Leila Hashemi-Beni

Keyword(s):

Deep Learning ◽

Water Depth ◽

Water Surface ◽

Point Cloud ◽

Depth Map ◽

Depth Estimation ◽

Surface Model ◽

Topographic Data ◽

Inundation Depth ◽

Uav Images

Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively.

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Sites of Infection by Pythium Species in Rice Seedlings and Effects of Plant Age and Water Depth on Disease Development

Phytopathology ◽

10.1094/phyto.1998.88.12.1255 ◽

1998 ◽

Vol 88 (12) ◽

pp. 1255-1261 ◽

Cited By ~ 16

Author(s):

S.-C. Chun ◽

R. W. Schneider

Keyword(s):

Biological Control ◽

Water Depth ◽

Disease Incidence ◽

Plant Age ◽

Infection Rates ◽

Seedling Disease ◽

Flood Water ◽

Short Period ◽

Seed Rot ◽

Growth Abnormalities

Seedling disease, caused primarily by several species of Pythium, is one of the major constraints to water-seeded rice production in Louisiana. The disease, also known as water-mold disease, seed rot, and seedling damping-off, causes stand reductions and growth abnormalities. In severe cases, fields must be replanted, which may result in delayed harvests and reduced yields. To develop more effective disease management tactics including biological control, this study was conducted primarily to determine sites of infection in seeds and seedlings; effect of plant age on susceptibility to P. arrhenomanes, P. myriotylum, and P. dissotocum; and minimum exposure times required for infection and seedling death. In addition, the effect of water depth on seedling disease was investigated. Infection rates of seed embryos were significantly higher than those of endosperms for all three Pythium spp. The development of roots from dry-seeded seedlings was significantly reduced by P. arrhenomanes and P. myriotylum at 5 days after planting compared with that of roots from noninoculated controls. Susceptibility of rice to all three species was sharply reduced within 2 to 6 days after planting, and seedlings were completely resistant at 8 days after planting. There was a steep reduction in emergence through the flood water, relative to the noninoculated control, following 2 to 3 days of exposure to inoculum of P. arrhenomanes and P. myriotylum. In contrast, P. dissotocum was much less virulent and required longer exposure times to cause irreversible seedling damage. Disease incidence was higher when seeds were planted into deeper water, implying that seedlings become resistant after they emerge through the flood water. These results suggest that disease control tactics including flood water management need to be employed for a very short period of time after planting. Also, given that the embryo is the primary site of infection and it is susceptible for only a few days, the disease should be amenable to biological control.

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