Prediction of Flood by Rainf All Using MLP Classifier of Neural Network Model

Haribabu, S.; Gupta, Gaurav; Kumar, P.Narendra; Rajendran, P. Selvi

doi:10.1109/icces51350.2021.9489161

Cited by 10 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Hosseini et al [42], short-term and long-term rainfall-runoff models' accuracy, precision, and performance were all improved by deconstructing ML algorithms (such as WNN). However, while WNNs have proven a success, longterm forecasts are limited.…”

Section: Analytical Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review

Aljohani,

Alkhodre,

Sen

et al. 2023

IJACSA

View full text Add to dashboard Cite

Flooding, caused by the overflow of water bodies beyond their natural boundaries, has severe environmental and socioeconomic consequences. To effectively predict and mitigate flood events, accurate and reliable flood modeling techniques are essential. This study provides a comprehensive review of the latest modeling techniques used in flood prediction, classifying them into two main categories: hydrologic models and machine learning models based on artificial intelligence. By objectively assessing the advantages and disadvantages of each model type, we aim to synthesize a systematic analysis of the various flood modeling approaches in the current literature. Additionally, we explore the potential of hybrid strategies that combine both modeling methods' best characteristics to develop more effective flood control measures. Our findings provide valuable insights for researchers and practitioners in the field of flood modeling, and our recommendations can contribute to the development of more efficient and accurate flood prediction systems.

show abstract

Section: Analytical Discussionmentioning

confidence: 99%

“…Through deconstruction, models performed far better in some circumstances, resulting in more accurate results. For example, wavelet-neuro-fuzzy models outperformed standalone ANFIS and ANNs in terms of accuracy and speed [42]. However, as the lead time lengthened, so did the degree of uncertainty in the predictions.…”

Section: Analytical Discussionmentioning

confidence: 99%

Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review

Aljohani,

Alkhodre,

Sen

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…During the 1990s, MLPs were widely employed in applications including speech recognition and picture classification [89]. MLPs can forecast the intensity of a flood occurrence based on past data and current weather conditions [90]. This can aid local governments and emergency services in better preparing for and responding to flood occurrences.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

View full text Add to dashboard Cite

Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

show abstract

“…According to existing studies, machine learning models are widely used in susceptibility prediction modeling due to their powerful ability to handle non-linear data with different scales and from different types of sources (Zhang et al, 2022). Recently, the research field of machine learning models has been rapidly expanding, with models such as multilayer perceptron (MLP) (Haribabu et al, 2021), support vector machine (SVM) (Xiong et al, 2019), logistic regression (LR) (Nguyen and Bouvier, 2019;Huang et al, 2020a), random forest (RF) (Abedi et al, 2022), decision trees (Ngo et al, 2021), and artificial neural networks (Dahri et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Chen

Zhang²,

Yang³

et al. 2023

Front. Earth Sci.

View full text Add to dashboard Cite

The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility.

show abstract

Prediction of Flood by Rainf All Using MLP Classifier of Neural Network Model

Cited by 10 publications

References 6 publications

Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review

Flood Prediction using Hydrologic and ML-based Modeling: A Systematic Review

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Contact Info

Product

Resources

About