Artificial neural network flood prediction for sungai isap residence

Keong, Khoo Chun; Mustafa, Mahfuzah; Mohammad, Ahmad Johari; Sulaiman, Mohd Herwan; Abdullah, Nor Rul Hasma

doi:10.1109/i2cacis.2016.7885321

Cited by 12 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The works implementing ANN are characterized for its simple implementation to predict values since it is possible the generation of an effective model to solve different issues as these models have series of time organized including suitable variables. This is observed in studies carried out in [13,14,15,16], and [17], where the authors demonstrate that using ANN to predict floods is adequate as the Root of the Square Mean Error (RSME) oscillates between 0.0007540 and 0.93. These studies were focused on those characteristics that might transform an artificial neuronal network into a more effective one; for instance, the analysis undertaken by Johannet et al [18], concluded that the results were improved when having hidden layers in the network.…”

Section: Introductionmentioning

confidence: 84%

“…In equation (13), the ( ) corresponds to the reference MSE as the obtained with the simplest model. The simplest possible model would be to always predict the average of all samples.…”

Section: Performance Metricmentioning

confidence: 99%

“…These studies were focused on those characteristics that might transform an artificial neuronal network into a more effective one; for instance, the analysis undertaken by Johannet et al [18] , concluded that the results were improved when having hidden layers in the network. Regarding the training algorithm, [13] concluded that the Bayesian Regularization algorithm presented a surpassing behavior. Additionally, the number of stations concur with the studies [19] and [14] in which only three measurement stations along the Kelantan and Phraya rivers were considered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Moreno

Sánchez

Espitia

2020

Heliyon

View full text Add to dashboard Cite

Floods are one of the worst natural disasters in the world. Colombia is a country that has been greatly affected by this disaster. For example, in the years 2010 and 2011 there was a heavy rainy season, which caused floods that affected at least two million people and there were economic losses of 6.5 million dollars, which is equivalent to 5.7% of the country's Gross Domestic Product (GDP) at that time. The Magdalena River is the most important since 128 municipalities and 43 cities with a population of 6.3 million people, which is 13% of the total population of the country, are located in its basins. For this reason, the objective of the research is to design and implement a model that helps predict flooding over the Magdalena River by examining three techniques of artificial intelligence (Artificial Neuronal Networks, Adaptive Neuro Fuzzy Inference System, Support Vector Machine), and thus determining which of these techniques are the most effective according to the case study. The research was limited only to these three types, due to limitations of time, data, human and financial resources, and technological infrastructure. In the end, it is concluded that the Artificial Neural Networks technique is a suitable option to implement the predictive system as long as it is not very complex and does not require high processing machine. However, to establish a model based on rules to achieve a better interpretability of the floods, the ANFIS model can be used.

show abstract

Section: Introductionmentioning

confidence: 84%

“…In equation (13), the ( ) corresponds to the reference MSE as the obtained with the simplest model. The simplest possible model would be to always predict the average of all samples.…”

Section: Performance Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Moreno

Sánchez

Espitia

2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…Three different optimization algorithms named LM, back-propagation, GD, and BR with back-propagation for optimizing the ANN result. The result of the prediction of BR is satisfactory [Keong et al 2016]. Eight different ML models are implemented and their results were compared.…”

Section: Related Workmentioning

confidence: 99%

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

Dubey

Katarya

2021

Preprint

View full text Add to dashboard Cite

Disaster is a very serious dissipation that arises for a short time period, but the impact of that disaster on human society is very dangerous and very long-lasting. Disasters are categorized into two types like natural disasters and manmade disasters. Among all disasters, of all the natural disasters, flood is the commonplace natural disaster. Flood disaster that causes huge loss of human life, diversity as well as economic loss, which is very dangerous for the developing countries and developed countries also. Nowadays during the monsoon season flood is dangerous for all the geographical areas located nearby water bodies. Much research has been done for flood detection. Machine Learning and many other recent technologies are playing a vital role in predicting the occurrence of floods. For prediction purposes, a huge amount of data is requiring collected from sensors deployed in various locations. In this paper, we used the Batch normalization with Deep Neural Network (BNDNN) technique for the classification of data in three classes as Low, Moderate, and High. The result obtained from our proposed model is compared with some other models like Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Deep Neural Network (DNN). In this our proposed BNDNN provides 89% accuracy which is higher among all existing models. Models are compared based on some parameters like Accuracy, Precision, Recall, F –Score. The compression among all the models used in this paper shows that our proposed model provides better results.

show abstract

“…Determination of the best ANN topology is important because it affects the weight and bias. Usually it performed by trial and error [19][20] or one-variable-at-time (OVAT) [21][22] where this procedure is very time-consuming and monotonous task. According to [23] for three different level of each ANN variables, about 245 (=3 5 ) different configuration of ANN would be required.…”

Section: Introductionmentioning

confidence: 99%

Optimization of artificial neural network topology for membrane bioreactor filtration using response surface methodology

Ibrahim

Wahab

Ismail

et al. 2020

IJ-AI

View full text Add to dashboard Cite

<p>The optimization of artificial neural networks (ANN) topology for predicting permeate flux of palm oil mill effluent (POME) in membrane bioreactor (MBR) filtration has been investigated using response surface methodology (RSM). A radial basis function neural network (RBFNN) model, trained by gradient descent with momentum (GDM) algorithms was developed to correlate output (permeate flux) to the four exogenous input variables (airflow rate, transmembrane pressure, permeate pump and aeration pump). A second-order polynomial model was developed from training results for natural log mean square error of 50 developed ANNs to generate 3D response surfaces. The optimum ANN topology had minimum ln MSE when the number of hidden neurons, spread, momentum coefficient, learning rate and number of epochs were 16, 1.4, 0.28, 0.3 and 1852, respectively. The MSE and regression coeffcient of the ANN model were determined as 0.0022 and 0.9906 for training, 0.0052 and 0.9839 for testing and 0.0217 and 0.9707 for validation data sets. These results confirmed that combining RSM and ANN was precise for predicting permeates flux of POME on MBR system. This development may have significant potential to improve model accuracy and reduce computational time.</p>

show abstract

Artificial neural network flood prediction for sungai isap residence

Cited by 12 publications

References 9 publications

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River

BNDNN: Batch Normalization Based Deep Neural Network for Predicting Flood in Urban Areas

Optimization of artificial neural network topology for membrane bioreactor filtration using response surface methodology

Contact Info

Product

Resources

About