FLOOD PREDICTION OF RIVER USING RAIN CLOUD IMAGES BY NEURAL NETWORK

2019 ◽  
Vol 75 (2) ◽  
pp. I_115-I_120
Author(s):  
Go OHNO ◽  
Kazunori ITO
Keyword(s):  
Neural Network ◽  
Flood Prediction ◽  
Rain Cloud

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

scholarly journals Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

2020 ◽  
Vol 5 (9) ◽  
pp. 1124-1130
Author(s):  
Ledisi Giok Kabari ◽  
Young Claudius Mazi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Neural Networks ◽  
Niger Delta ◽  
Feedforward Neural Networks ◽  
Flood Prediction ◽  
The Neural Network ◽  
Significant Damage ◽  
Weather Underground ◽  
Short Term Prediction

Climate change generates so many direct and indirect effects on the environment.  Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta   sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com.  An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta   sub region of Nigeria.

scholarly journals Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2271
Author(s):  
Yoon Ha Lee ◽  
Hyun Il Kim ◽  
Kun Yeun Han ◽  
Won Hwa Hong
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Flood Risk Assessment ◽  
Flood Prediction ◽  
Prone Area ◽  
Auto Regressive ◽  
Flood Prone Area ◽  
Hazard Level ◽  
Evacuation Routes ◽  
Evacuation Route

For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions.

scholarly journals Study of Correlations between Climatic Parameters and Flood of the Maroon River Basin and flood Prediction by Smart Neural Network

2018 ◽  
Vol 8 (16) ◽  
pp. 44-52
Author(s):  
صمد فتوحی ◽  
خدیجه جوجی زاده ◽  
مریم نصیری ◽  
ناصر اورک ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
River Basin ◽  
Climatic Parameters ◽  
Flood Prediction ◽  
Maroon River

scholarly journals A Promising Wavelet Decomposition –NNARX Model to Predict Flood

2020 ◽  
Vol 11 (2) ◽  
pp. 89-99
Author(s):  
Mohd Azrol Syafiee Anuar ◽  
Ribhan Zafira Abdul Rahman ◽  
Azura Che Soh ◽  
Samsul Bahari Mohd Noor ◽  
Zed Diyana Zulkafli
Keyword(s):  
Neural Network ◽  
Flow Rate ◽  
Wavelet Decomposition ◽  
Performance Testing ◽  
Original Data ◽  
Flood Prediction ◽  
The Neural Network ◽  
Major Disaster ◽  
A New Technique

Flood is a major disaster that happens around the world. It has caused many casualties and massive destruction of property. Estimating the chance of a flood occurring depends on several factors, such as rainfall, the structure and the flow rate of the river. This research used the neural network autoregressive exogenous input (NNARX) model to predict floods. One of the research challenges was to develop accurate models and improve the forecasting model. This research aimed to improve the performance of the neural network model for flood prediction. A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach. This paper discusses the process of identifying the parameters involved to make a forecast as the rainfall value requires the flow rate of the river and its water level. The original data were processed by wavelet decomposition and filtered to generate a new set of data for the NNARX prediction model where the process can be compared. This research compared the performance of the wavelet and the non-wavelet NNARX model. Experimental results showed that the proposed approach had better performance testing results in relation to its counterpart in terms of hourly forecast, with the mean square error (MSE) of 2.0491e-4 m2 compared to 6.1642e-4 m2, respectively. The proposed approach was also studied for long-term forecast up to 5 years, where the obtained MSE was higher, i.e., 0.0016 m2.

Sign in / Sign up

Export Citation Format

Share Document