Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models

• URL: http://arxiv.org/abs/2112.12510v1
• Date: Wed, 22 Dec 2021 11:25:23 GMT
• Title: Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models
• Authors: Rados{\l}aw Szostak, Marcin Pietro\'n, Miros{\l}aw Zimnoch, Przemys{\l}aw Wachniew, Pawe{\l} \'Cwi\k{a}ka{\l}a, Edyta Puniach
• Abstract summary: Machine learning was used to extract a Water Surface Elevation (WSE) value from disturbed photogrammetric data. Data can be used to validate and calibrate hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Development of the new methods of surface water observation is crucial in the perspective of increasingly frequent extreme hydrological events related to global warming and increasing demand for water. Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the Water Surface Elevation (WSE) of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. In this study, machine learning was used to extract a WSE value from disturbed photogrammetric data. A brand new dataset has been prepared specifically for this purpose by hydrology and photogrammetry experts. The new method is an important step toward automating water surface level measurements with high spatial and temporal resolution. Such data can be used to validate and calibrate of hydrological, hydraulic and hydrodynamic models making hydrological forecasts more accurate, in particular predicting extreme and dangerous events such as floods or droughts. For our knowledge this is the first approach in which dataset was created for this purpose and deep learning models were used for this task. Additionally, neuroevolution algorithm was set to explore different architectures to find local optimal models and non-gradient search was performed to fine-tune the model parameters. The achieved results have better accuracy compared to manual methods of determining WSE from photogrammetric DSMs.

Related papers

• Beyond Tides and Time: Machine Learning Triumph in Water Quality [0.0]
  This study aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge. Our research aims to establish a robust predictive pipeline to both data science experts and those without domain specific knowledge.
  arXiv  Detail & Related papers  (2023-09-29T03:33:53Z)
• DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation [44.99833362998488]
  We present DeepAqua, a self-supervised deep learning model that eliminates the need for manual annotations during the training phase. We exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. Experimental results show that DeepAqua outperforms other unsupervised methods by improving accuracy by 7%, Intersection Over Union by 27%, and F1 score by 14%.
  arXiv  Detail & Related papers  (2023-05-02T18:06:21Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space [67.07476542850566]
  We propose to model the quality degradation in perceptual space from a statistical distribution perspective. The quality is measured based upon the Wasserstein distance in the deep feature domain. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination.
  arXiv  Detail & Related papers  (2022-08-05T02:46:12Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• High-resolution rainfall-runoff modeling using graph neural network [0.0]
  We propose a novel deep learning model that makes full use of spatial information from high-resolution precipitation data. GNRRM has less over-fitting and significantly improves model performance.
  arXiv  Detail & Related papers  (2021-10-21T00:12:02Z)
• Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network [55.41644538483948]
  We propose to aggregate simulations of lake temperature at a certain depth together with a range of meteorological features. This work demonstrates that the proposed model can deliver homogeneously good performance covering the whole lake surface. Results are compared with a state-of-the-art Bayesian deep learning method.
  arXiv  Detail & Related papers  (2021-09-27T09:19:53Z)
• Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM [0.0]
  In this paper, a multi-layer perceptron is applied to simulate groundwater level. The adaptive moment estimation algorithm is also used to this matter. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction.
  arXiv  Detail & Related papers  (2021-07-29T10:11:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.