River Water Level Prediction Based on Deep Learning: Case Study on the Geum River, South Korea

2021 ◽  
pp. 319-325
Author(s):  
Xuan-Hien Le ◽  
Sungho Jung ◽  
Minho Yeon ◽  
Giha Lee
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Water Level ◽  
River Water ◽  
Water Level Prediction

River Water Level Prediction Using Passive Microwave Signatures—A Case Study: The Bermejo Basin

2014 ◽  
Vol 7 (9) ◽  
pp. 3903-3914 ◽  
Author(s):  
Cristina Vittucci ◽  
Leila Guerriero ◽  
Paolo Ferrazzoli ◽  
Rachid Rahmoune ◽  
Veronica Barraza ◽  
...  
Keyword(s):  
Water Level ◽  
River Water ◽  
Passive Microwave ◽  
Bermejo Basin ◽  
Microwave Signatures ◽  
Water Level Prediction

River Water Level Prediction in Coastal Catchment using hybridized relevance vector machine model with improved grasshopper optimization

2021 ◽  
pp. 126477
Author(s):  
Hai Tao ◽  
Najah Kadhim Al-Bedyry ◽  
Khaled Mohamed Khedher ◽  
Shamsuddin Shahid ◽  
Zaher Mundher Yaseen
Keyword(s):  
Water Level ◽  
River Water ◽  
Relevance Vector Machine ◽  
Machine Model ◽  
Grasshopper Optimization ◽  
Water Level Prediction

scholarly journals RIVER WATER LEVEL PREDICTION FROM RAINFALL

2010 ◽  
Vol 66 (1) ◽  
pp. 93-98
Author(s):  
Toru HIRAOKA ◽  
Masataka IKARI ◽  
Hiromi YUKI
Keyword(s):  
Water Level ◽  
River Water ◽  
Water Level Prediction

Urban Water Flow and Water Level Prediction Based on Deep Learning

2017 ◽  
pp. 317-329 ◽  
Author(s):  
Haytham Assem ◽  
Salem Ghariba ◽  
Gabor Makrai ◽  
Paul Johnston ◽  
Laurence Gill ◽  
...  
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
Water Flow ◽  
Urban Water ◽  
Water Level Prediction

scholarly journals Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera

2021 ◽  
Vol 11 (20) ◽  
pp. 9691
Author(s):  
Nur Atirah Muhadi ◽  
Ahmad Fikri Abdullah ◽  
Siti Khairunniza Bejo ◽  
Muhammad Razif Mahadi ◽  
Ana Mijic
Keyword(s):  
Deep Learning ◽  
Water Level ◽  
River Water ◽  
Correlation Coefficient ◽  
Rank Order ◽  
Measurement Data ◽  
Strong Relationship ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Surveillance Systems

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.

Data augmentation using a variational autoencoder for estimating property prices

2021 ◽  
Vol 39 (3) ◽  
pp. 408-418 ◽  
Author(s):  
Changro Lee
Keyword(s):  
Deep Learning ◽  
South Korea ◽  
Data Augmentation ◽  
Model Performance ◽  
Content Type ◽  
Original Dataset ◽  
Learning Techniques ◽  
Variational Autoencoder ◽  
Price Estimation

PurposePrior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques.Design/methodology/approachThe deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance.FindingsThe results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry.Originality/valueAlthough deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation.

Development of a Practical River Water Level Prediction Method Using Data Assimilation Technique

2019 ◽  
Vol 14 (2) ◽  
pp. 260-268 ◽  
Author(s):  
Shuichi Tsuchiya ◽  
◽  
Masaki Kawasaki
Keyword(s):  
Data Assimilation ◽  
Prediction Model ◽  
Water Level ◽  
River Water ◽  
Channel Model ◽  
Prediction Method ◽  
Water Levels ◽  
Using Data ◽  
Data Assimilation Technique ◽  
Water Level Prediction

With the aim of accurately predicting river water levels a few hours ahead in the event of a flood, we created a river water level prediction model consisting of a runoff model, a channel model, and data assimilation technique. We also developed a cascade assimilation method that allows us to calculate assimilations of water levels observed at multiple points using particle filters in real-time. As a result of applying the river water level prediction model to Arakawa Basin using the assimilation technique, it was confirmed that reproductive simulations that produce results very similar to the observed results could be achieved, and that we would be able to predict river water levels less affected by the predicted amount of rainfall.

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