Flood Prediction Model using Artificial Neural Network

Paul, Abhijit; Das, Prodipto

doi:10.7753/ijcatr0307.1016

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…The result is close to the real data [34]. In [35], the flood prediction employing artificial neural network was described, and the flood water level was successfully predicted 24 hours, 48 hours, and 72 hours ahead of time. The results showed MAE varying from 0.6 to 0.9 and RMSE varying from 0.05 to 0.11.…”

Section: Introductionsupporting

confidence: 65%

“…Compared with the other methods presented in the literature using other databases, the proposed hybrid model provides reliable flood vulnerability forecasting, as shown in Table 9. In [35], the accuracy obtained in terms of MAE from 0.627 to 0.9357 and RMSE from 0.0523 to 0.1154 for flood prediction of 24 hours to 72 hours ahead of time. In [36], the accuracy of the average RMSE was 0.367% for flood forecasting in Tancheon Basin in Korea.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 65%

Section: Resultsmentioning

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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“…Then, the comparison between predicted values of the outputs and the actual values is performed, and the difference between them is termed an error. The weights w ji and w jk , which connect input layers to hidden layers and hidden layers to output layers, are adjusted by backpropagating the error calculated through the error function [40] (8):…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China

Gulshad

Wang

et al. 2022

Land

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Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

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“…The Feed Forward (FF) and Back Propagation (BP) algorithms of Multi Linear Perceptron (MLP) are used to predict floods and compare the predicted water level to the actual water level using our simulation results. From the results, it is clear that the developed model successfully predicts the flood water level 24 hours ahead of time [4]. Nghiem Van Tinh (2019) developed a flood forecasting model based on an artificial neural network was developed.…”

Section: Introductionmentioning

confidence: 99%

Flood Forecasting Using Artificial Neural Network

Mistry

Parekh

2022

IOP Conf. Ser.: Earth Environ. Sci.

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The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model development. The developed model is validated to test the accuracy of the model. Trained and validated models are evaluated using performance indices. Six alternative flood prediction models have been developed using ANN. These models are developed based on various training algorithms. A single layer feed forward back-propagation neural network with six different training algorithms (Scaled conjugate gradient, Levenberg Marquardt, Resilient back-propagation, Conjugate gradient, and Cascade forward back propagation, Bayesian regularization) was developed, with 70% of the data used for training and 30% for validation. The created models’ performance is assessed using statistical performance parameters. The best performance was obtained with an ANN model developed using the Cascade forward back-propagation training algorithm, which had a coefficient of correlation (r) of 0.83, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 5.58 for training and a coefficient of correlation (r) of 0.89, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 7.27 for validation. The forecast inflow is very close to the observed values. This study shows that ANN can be used to successfully predict floods, and the model developed can be used by flood control departments across the country for flood forecasting.

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Flood Prediction Model using Artificial Neural Network

Cited by 16 publications

References 16 publications

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

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

Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China

Flood Forecasting Using Artificial Neural Network

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