The prediction of water level based on support vector machine under construction condition of steel sheet pile cofferdam

2020 ◽  
Author(s):  
Jianjun Wang ◽  
Zijie Jiang ◽  
Fan Li ◽  
Weiming Chen
Keyword(s):  
Support Vector Machine ◽  
Water Level ◽  
Steel Sheet ◽  
Support Vector ◽  
Sheet Pile ◽  
Under Construction ◽  
Construction Condition

Simulating Caspian Sea surface water level by artificial neural network and support vector machine models

2020 ◽  
Vol 68 (2) ◽  
pp. 553-563 ◽  
Author(s):  
M. R. Khaledian ◽  
M. Isazadeh ◽  
S. M. Biazar ◽  
Q. B. Pham
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Surface Water ◽  
Water Level ◽  
Caspian Sea ◽  
Sea Surface ◽  
Support Vector ◽  
Artificial Neural

A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm

2015 ◽  
Vol 270 ◽  
pp. 731-743 ◽  
Author(s):  
Ozgur Kisi ◽  
Jalal Shiri ◽  
Sepideh Karimi ◽  
Shahaboddin Shamshirband ◽  
Shervin Motamedi ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Water Level ◽  
Firefly Algorithm ◽  
Water Level Fluctuation ◽  
Urmia Lake ◽  
Support Vector ◽  
Level Fluctuation

scholarly journals Overwhelming Flow of Water using Machine Learning Techniques

2019 ◽  
Vol 9 (2S3) ◽  
pp. 593-601
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Water Level ◽  
Flood Forecasting ◽  
Ultrasonic Sensor ◽  
Rainfall Data ◽  
Support Vector ◽  
Artificial Neural Network Ann

Floods are rare and dangerous disaster in minimum duration, which have the most destructive impact within urban and rural areas. This research in flood prediction models contributed to risk reduction, to prevent the loss of human life, and reduce the property of damage in floods. This study implements the automated machine learning models, using the Support Vector Machine (SVM) and Artificial Neural Network (ANN). The rainfall data and various meteorological parameter which include temperature data are used in this study. Concurrent daily records of inflow and discharge are taken into consideration to calculate the water level to quantify the importance of the lake flow. It aims to discovering accurate and efficient for the flood forecasting model. This paper attempts to forecast flood by modelling water level, temperature and rainfall data in the region of Korattur lake, Chennai, India. In this study, ultrasonic sensor used to capture the measurement of water level to predict from ultrasonic waves and input of same implemented in BPNN and Support Vector Machine (SVM) were used for flood forecasting. The water level flow is deducted in this research using ultrasonic sensor, proves the best efficient models applied for flood forecasting. This study can be used as a predicting the flood by choosing the proper Machine Learning (ML) algorithm such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithm for showing higher accuracy. To get more accurate result of the models, three standard statistical performance evaluation parameters, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination ( ) were used to analyse the performance of the model developed. As a result, the proposed model proves the most efficiency and accuracy for predicting the flood forecasting

Application of Support Vector Machine in Lake Water Level Prediction

2006 ◽  
Vol 11 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Mohammad Sajjad Khan ◽  
Paulin Coulibaly
Keyword(s):  
Support Vector Machine ◽  
Water Level ◽  
Lake Water ◽  
Support Vector ◽  
Lake Water Level ◽  
Water Level Prediction

Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network

2014 ◽  
Vol 41 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Mojtaba Noury ◽  
Hosein Sedghi ◽  
Hosein Babazedeh ◽  
Hedayat Fahmi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Water Level ◽  
Lake Water ◽  
Water Level Fluctuation ◽  
Urmia Lake ◽  
Support Vector ◽  
Level Fluctuation ◽  
Lake Water Level

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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