Wireless sensor nodes for flood forecasting using artificial neural network

Sahagun, Mary Anne M.; Cruz, Jennifer C. Dela; Garcia, Ramon G.

doi:10.1109/hnicem.2017.8269462

Cited by 16 publications

(11 citation statements)

References 14 publications

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“…Regarding the exogenous inputs, overflow average and the registry of rains were applied; thus, from the total data 70% was taken to train the NNARX, and the remaining 30% to perform tests. Both, [28] and [29] comparison performances of NNARX together with the ANN and NNAR were made to confirm that the best results were obtained with the first technique.…”

Section: Introductionmentioning

confidence: 97%

“…In this regard, [25] implemented data of the Kelang river to predict the level of water for three hours in advance; the same study compared four different cases in which the variation was given in terms of time; besides, different algorithms were carried out: Gradient Descent (GD), Levenberg-Marquardt (LM) and One Step Secant (OSS). The LM training algorithm was used in [26] showing a suitable performance, in [27] a comparison of its performance with that of the Bayesian Regularization was performed, while [28] found that the GD algorithm presented a better performance compared to the algorithm OSS.…”

Section: Introductionmentioning

confidence: 99%

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

Moreno

Sánchez

Espitia

2020

Heliyon

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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.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Moreno

Sánchez

Espitia

2020

Heliyon

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“…The hardware setup contains a microcontroller, solar penal, ultrasonic sensor, and GSM module. A Feedforward network with backpropagation is used and for optimizing the network Levenberg-Marquardt training algorithm is used [Sahagun et al 2017]. In some cases, a tree-based ML model is also used to predict the sensitivity of flooded areas based on the spatial parameters [Lee et al 2017].…”

Section: Related Workmentioning

confidence: 99%

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

Dubey

Katarya

2021

Preprint

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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.

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“…In [22], flood events were predicted using numerical weather prediction (NWP) with improved accuracy. In [23], the main objective is to predict the high-risk area by water level using Artificial Neural Network in Masantol, Pampanga. In [24], a flood event prediction model based on SVM and boosting algorithm was presented.…”

Section: Introductionmentioning

confidence: 99%

Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

Supatmi

Sumitra

2019

Computational Intelligence and Neuroscience

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An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

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Wireless sensor nodes for flood forecasting using artificial neural network

Cited by 16 publications

References 14 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

Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

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