EXAMINATION OF SHORT-TERM WATER LEVEL PREDICTION MODEL IN LOW-LYING LAKES USING MACHINE LEARNING

2020 ◽  
Vol 76 (2) ◽  
pp. I_439-I_444
Author(s):  
Masaomi KIMURA ◽  
Takahiro ISHIKAWA ◽  
Naoto OKUMURA ◽  
Issaku AZECHI ◽  
Toshiaki IIDA
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Water Level ◽  
Short Term ◽  
Water Level Prediction

Short-Term Water Level Prediction in Middle Stream of Yangtze River

2014 ◽  
Vol 1065-1069 ◽  
pp. 2983-2988
Author(s):  
Hai Qiang Hou ◽  
Xing Long Liu ◽  
Wu Xiong Xu ◽  
Huai Han Liu
Keyword(s):  
Prediction Model ◽  
Water Level ◽  
Multiple Regression ◽  
Yangtze River ◽  
Short Term ◽  
Middle Stream ◽  
River Reach ◽  
Short Term Prediction ◽  
Measured Water ◽  
Water Level Prediction

Based on the measured water level data after the impound of Three Gorges reservoir, the water level short-term prediction model of income flow of Chenglingji, Han river and Hukou is constructed by multiple regression method. The comparative of measured water level and predicted water level indicated that, the prediction of income flow is accord with the real flow. Meanwhile, according to statistical analysis of the water level and flow, and considering the total inflow and the jacking of branch inflow, the water level short-term prediction model for middle stream Yangtze River is set up separately. Then, by using multiple regression model, the multiple regression formula for water level prediction is constructed , to applied to the river reach where branch inflowed or river reach jacked by the downstream. Compared with the field observation data, the prediction results are quite precisely.

Machine Learning Based Weather Prediction Model for Short Term Weather Prediction in Sri Lanka

2021 ◽  
Author(s):  
K.M.S.A. Hennayake ◽  
Randima Dinalankara ◽  
Dulini Yasara Mudunkotuwa
Keyword(s):  
Machine Learning ◽  
Sri Lanka ◽  
Prediction Model ◽  
Weather Prediction ◽  
Short Term

Short-term water level prediction using neural networks and neuro-fuzzy approach

2003 ◽  
Vol 55 (3-4) ◽  
pp. 439-450 ◽  
Author(s):  
Bunchingiv Bazartseren ◽  
Gerald Hildebrandt ◽  
K.-P. Holz
Keyword(s):  
Neural Networks ◽  
Water Level ◽  
Fuzzy Approach ◽  
Short Term ◽  
Neuro Fuzzy ◽  
Water Level Prediction

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.

scholarly journals Water Level Prediction Model Based on GRU and CNN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 60090-60100 ◽  
Author(s):  
Mingyang Pan ◽  
Hainan Zhou ◽  
Jiayi Cao ◽  
Yisai Liu ◽  
Jiangling Hao ◽  
...  
Keyword(s):  
Prediction Model ◽  
Water Level ◽  
Model Based ◽  
Water Level Prediction
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