scholarly journals Multistep Flood Inundation Forecasts with Resilient Backpropagation Neural Networks: Kulmbach Case Study

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3568
Author(s):  
Qing Lin ◽  
Jorge Leandro ◽  
Stefan Gerber ◽  
Markus Disse
Keyword(s):  
Neural Networks ◽  
Flood Hazard ◽  
Forecast Accuracy ◽  
Threshold Value ◽  
Flood Inundation ◽  
Flood Events ◽  
Forecast Performance ◽  
Flooding Event ◽  
Good Forecast

Flooding, a significant natural disaster, attracts worldwide attention because of its high impact on communities and individuals and increasing trend due to climate change. A flood forecast system can minimize the impacts by predicting the flood hazard before it occurs. Artificial neural networks (ANN) could efficiently process large amounts of data and find relations that enable faster flood predictions. The aim of this study is to perform multistep forecasts for 1–5 h after the flooding event has been triggered by a forecast threshold value. In this work, an ANN developed for the real-time forecast of flood inundation with a high spatial resolution (4 m × 4 m) is extended to allow for multiple forecasts. After trained with 120 synthetic flood events, the ANN was first tested with 60 synthetic events for verifying the forecast performance for 3 h, 6 h, 9 h and 12 h lead time. The model produces good results, as shown by more than 81% of all grids having an RMSE below 0.3 m. The ANN is then applied to the three historical flood events to test the multistep inundation forecast. For the historical flood events, the results show that the ANN outputs have a good forecast accuracy of the water depths for (at least) the 3 h forecast with over 70% accuracy (RMSE within 0.3 m), and a moderate accuracy for the subsequent forecasts with (at least) 60% accuracy.

scholarly journals Reconstituting past flood events: the contribution of citizen science

2019 ◽  
Author(s):  
Bocar Sy ◽  
Corine Frischknecht ◽  
Hy Dao ◽  
David Consuegra ◽  
Gregory Giuliani
Keyword(s):  
Citizen Science ◽  
Local Community ◽  
Flood Hazard ◽  
Quantitative Information ◽  
Flood Events ◽  
Flood Impacts ◽  
Causative Factors ◽  
Instrumental Records ◽  
Different Parts

Abstract. Information gathered on past flood events is essential for understanding and assessing flood hazard. In this study, we present how citizen science can help retrieving this information, in particular in areas with scarce or no instrumental measurements on past events. The case study is located in Yeumbeul North (YN), Senegal, where flood impacts represent a growing concern for the local community. This area lacks instrumental records on flood extent and water depth as well as information on the chain of causative factors. We developed a framework using two techniques to retrieve information on past flood events by involving two groups of citizens who were present during the floods. The first technique targeted the part of the citizens’ memory, which records information on events, recalled through narratives, whereas the second technique focused on scaling past flood event intensities using different parts of the witnesses’ body. These techniques were used for 3 events, which occurred in 2005, 2009 and 2012. They proved complementary by providing quantitative information on flood extents and water depths, and by revealing factors that may have contributed in aggravating floods for 3 events which occurred in 2005, 2009 and 2012.

Comparative Flood Inundation Mapping Utilizing Multi-Temporal Optical and SAR Satellite Data Over North Bihar Region

2020 ◽  
pp. 149-168 ◽  
Author(s):  
Gaurav Tripathi ◽  
Arvind Chandra Pandey ◽  
Bikash Ranjan Parida ◽  
Achala Shakya
Keyword(s):  
River Basins ◽  
Flood Inundation ◽  
Flood Events ◽  
Inundation Mapping ◽  
Flood Inundation Mapping ◽  
Multi Temporal ◽  
Inundation Extent ◽  
Present Case Study ◽  
Gandak River

Floods are investigated to be the utmost frequent and destructive phenomena among all other types of natural calamities worldwide. Thus, flood events need to be mapped to understand their impact on the affected region. The present case study is intended to examine and analyze the flood events occurred in July-August 2019 over the Northern Bihar region situated in Kosi and Gandak river basins. Furthermore, a comparative study was carried out to map the satellite based near real time flood inundation using multi-temporal Sentinel–1A (SAR) and MODIS NRT Flood data (optical and 3-day composite). Optical (MODIS) and Sentinel-1 SAR data were acquired to compare their flood inundation extent and the result shows overestimation in MODIS flood data due to varying spatial resolutions.

scholarly journals FLOOD HAZARD MAPPING OF BOGOWONTO RIVER IN PURWOREJO REGENCY, CENTRAL JAVA PROVINCE

2017 ◽  
Vol 2 (3) ◽  
pp. 243 ◽  
Author(s):  
Margaretha Titi Pawestri ◽  
Joko Sujono ◽  
Istiarto Istiarto
Keyword(s):  
Flood Hazard ◽  
Hazard Mapping ◽  
Flood Inundation ◽  
Hazard Maps ◽  
Flood Events ◽  
Hydraulic Modelling ◽  
Surrounding Area ◽  
Huge Amount ◽  
Central Java ◽  
Starting Point

The overflowing discharge of Bogowonto River in Purworejo Regency, Central Java flooded the surrounding area during the rainy season. A huge amount of losses such as damage of infrastructures, housing, and agricultural area occurs every year. This research mainly aims to develop flood hazard map and study the characteristics of flood in the study area. There are two main analysis; hydrologic and hydraulic, to model a flood event. Hydrologic and hydraulic modelling of flood based on 20 and 50- years return period hydrograph along the river geometryis done using the latest HEC program namely HEC- HMS 4.1 and HECRAS 5.0. Also, ArcGIS 10.3 is used as a terrain pre-processor and post-processor for hazard mapping. The results of this research are flood hazard maps for 20 and 50 years flood and its comparison to the recent major flood events. Flood inundation modelled covered an area of 993.77 Ha and 1,175.86 Ha, with maximum discharge calculated at Boro Weir as starting point are 1206.2 m3/s and 1,397.3 m3/s for 20 and 50 years flood case, respectively.

scholarly journals Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

2020 ◽  
Author(s):  
Peter Uhe ◽  
Daniel Mitchell ◽  
Paul D. Bates ◽  
Nans Addor ◽  
Jeff Neal ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
River Flow ◽  
Flood Hazard ◽  
Data Sets ◽  
Flood Inundation ◽  
River Flooding ◽  
Global Data Sets ◽  
Global Data

Abstract. There is an urgent need for the climate community to translate their meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities as we seek to understand how anthropogenic climate change has, and will, impact our society. This can be particularly challenging because there are often multiple specialized steps to model the hazard. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (~ 90 m) river flooding (fluvial) hazards. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be directly related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data-sets and thus can be applied anywhere in the world, but we use the Brahmaputra river in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework.

Multi-Criteria Decision Making for Vulnerability Mapping of Flood Hazard: A Case Study of Pune City

2019 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Nitin Mundhe
Keyword(s):  
Decision Making ◽  
Flood Hazard ◽  
Geographical Information ◽  
Multi Criteria Decision Making ◽  
Flood Hazards ◽  
Very High Frequency ◽  
Flood Vulnerability ◽  
Natural Risk ◽  
The Town

Floods are natural risk with a very high frequency, which causes to environmental, social, economic and human losses. The floods in the town happen mainly due to human made activities about the blockage of natural drainage, haphazard construction of roads, building, and high rainfall intensity. Detailed maps showing flood vulnerability areas are helpful in management of flood hazards. Therefore, present research focused on identifying flood vulnerability zones in the Pune City using multi-criteria decision-making approach in Geographical Information System (GIS) and inputs from remotely sensed imageries. Other input data considered for preparing base maps are census details, City maps, and fieldworks. The Pune City classified in to four flood vulnerability classes essential for flood risk management. About 5 per cent area shows high vulnerability for floods in localities namely Wakdewadi, some part of the Shivajinagar, Sangamwadi, Aundh, and Baner with high risk.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural
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