Flood Modelling and Prediction Using Artificial Neural Network

Sanubari, Awal Rais; Kusuma, Purba Daru; Setianingsih, Casi

doi:10.1109/iotais.2018.8600869

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…The hidden layer is responsible for conducting computations on the input data and subsequently transmitting the resulting output to the output layer. The factors encompassed in this context are weight, activation function and cost function [17].…”

Section:  Noise Reductionmentioning

confidence: 99%

Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

Zulkiflee,

Mohd Safar,

Kamaludin

et al. 2024

JOIV : Int. J. Inform. Visualization

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This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology.

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Section:  Noise Reductionmentioning

confidence: 99%

Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

Zulkiflee,

Mohd Safar,

Kamaludin

et al. 2024

JOIV : Int. J. Inform. Visualization

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show abstract

“…Sanubari et al [4] show that water levels and rainfall data across the river are two metrics that can be utilized to forecast floods. He has proposed a system of the artificial neural network to explore flood prediction capacity by using an artificial neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ullah and Choudhury [11] have used the ANFIS, a model, for anticipating flood monitoring and forecasting during a waterway framework. This investigation considers three unmistakable ANFIS model sorts together profundity, profundity release a lot release the information on stream and stream profundities are haphazardly ordered into various gatherings (2,3,4,6) and different information-driven quantities of participation capacities (Triangular, Gaussian, Trapezoidal, and Bell), with two classes of Gaussian info and consistent yield enrollment capacities being picked by experimentation. The Mahi River is 583 kilometers long, 167 kilometers in Madhya Pradesh, 174 kilometers in Rajasthan, and 242 kilometers in Gujarat.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced Techniques of Flood Forecasting, Flood Inundation Mapping and Flood Prioritization of Panam River Basin

Patel¹,

Parekh²

2022

cea

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“…The major drawback of these works is the inadequate data set. Artificial neural network model was adopted by [4,49,50] which accepts input features or parameters such as rainfall data, water level, hygrometric data, temperature and information on dam operation [48] The evaluation was done with accuracy and mean absolute percentage error of relatively good performance. The main drawback of these studies was high computational cost due to use of artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Flood Image Classification using Convolutional Neural Networks

Adetunji,

Ibrahim Adepoju Adeyanju,

Esan

et al. 2023

AJERD

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Flood disaster is a natural disaster that leads to loss of lives, properties damage, devastating effects on the economy and environment; therefore, there should be effective predictive measures to curb this problem. Between the years 2002- 2023, flood has caused death of over 200,000 people globally and occurred majorly in resource poor countries and communities. Different machine learning approaches have been developed for the prediction of floods. This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. Important parameters such as standard deviation and variance were incorporated in the parameters tuned CNN model that performed flood images feature extraction and classification for better predictive performance. The enhanced model was assessed with accuracy and loss measurement and compared with the existing model. The model leverage on the unique features of region of Interest aligns to resolve the issues of misalignments caused by the use of region of Interest pooling engaged in the traditional Faster-RCNN. The techniques and the developed system were implemented using a Python-based integrated development environment called “Anaconda Navigator” on Intel Core i5 with 8G Ram hardware of Window 10 operating system. The developed model achieved optimal accuracy at 200 epochs with 99.80% and corresponding loss of 0.0890. The results confirmed that predictive performance of a model can be improved by incorporating standard deviation and variance on model, coupled with its parameters tunning approach before classification.

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Flood Modelling and Prediction Using Artificial Neural Network

Cited by 8 publications

References 9 publications

Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin

Advanced Techniques of Flood Forecasting, Flood Inundation Mapping and Flood Prioritization of Panam River Basin

Flood Image Classification using Convolutional Neural Networks

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