Flood Prediction Using Hybrid ANFIS-ACO Model: A Case Study

Agnihotri, Ankita; Sahoo, Abinash; Diwakar, Manoj Kumar

doi:10.1007/978-981-16-6723-7_13

Cited by 25 publications

(5 citation statements)

References 27 publications

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“…Hybrid Machine Learning models are employed to improve the performance and efficiency of flood prediction models like Bidirectional Gated Recurrent Unit (BiGRU) multi-step [15] , LSTM–GRU-based model [16] and adaptive neuro-fuzzy inference system (ANFIS) combined with ant colony optimization (ACO) algorithm which optimize model parameters for predicting flood accurately [17] .…”

Section: Literature Reviewmentioning

confidence: 99%

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

H V,

Rao,

et al. 2023

MethodsX

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Section: Literature Reviewmentioning

confidence: 99%

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

H V,

Rao,

et al. 2023

MethodsX

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“…Ankita Agnihotri et al, exhibited the capacity of hybrid optimization algorithm in further developing accuracy of typical ANFIS for flood forecasting. Capability of mixture flood estimating model is contrasted and independent ANFIS in view of quantitative factual lists like coefficient of assurance (R2), Mean Outright Mistake (MAE) and Root Mean Square Blunder (RMSE) [2]. Parag Ghorpade et al, has shown that how machine learning algorithms have recently improved flood forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of Flood Flow of Panam River Basin using Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN with Comparative Study

Monal Patel,

Falguni Parekh

2023

ARASET

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Flood forecasting is one of the most important issues in the hydrology due to its indispensable contribution in lowering monetary and life losses. In recent years reliability of flood forecasting using various modelling tools has improved to a great extent due to hydrologic modelling, development in expertise and knowledge, and advancement in the collection of data and algorithms for evaluation. The current study presents the use of the Adaptive Neuro Fussy Inference System (ANFIS) in forecasting floods for the Panam River basin system. ANFIS combines neural network algorithms and fuzzy reasoning to map an input space to an output space. This paper includes the development of ANFIS models using various membership function and their comparison for forecasting the inflow rate into the Panam dam, which creates flooding conditions on the downstream side. The different evaluation parameters like Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R2) and Discrepancy Ratio (D) are used to evaluate the results of each model. From all the developed ANFIS models, the best ANFIS model is selected, having RMSE as 271.91, R as 0.98, R2 as 0.96, and D as 1.00 for training the model, and RMSE as 2000.74, R as 0.95, R2 as 0.90 and D as 1.12 for validating the model. Artificial Neural Network (ANN) model has also been developed for forecasting flood. In the Neural network there are total 3 types of transfer function in each layer i.e., LOGSIG, TANSIG and PURELIN. All the developed is evaluated with the coefficient of correlation (R) and Mean Squared Error (MSE). In the end, future headings for innovative research work are discussed.

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“…One of the major requirements for developing countries is for the technology to be affordable as their national income does not allow them to overspend. India is one such country that needs to resort to certain technological and mathematical models in predicting floods [9]. The aim of the present paper is to review existing technology that can be used to successfully predict and manage floods in India.…”

Section: Introductionmentioning

confidence: 99%

Reviewing the use of technology to manage floods in India

Adwithiya Pachisia,

Ishita Verma,

Preethi B Meena

2024

World J. Adv. Eng. Technol. Sci.

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The present article is a review of existing technology that can help predict as well as manage floods in India. Since some of the major metropolitan cities in the country have been regularly experiencing floods over the past few decades, this not only puts property at risk, but also increases the probability of loss of life due to the floods. Today, novel techniques of predicting floods have replaced the traditional ones. One such technology is the use of artificial intelligence. This has been highlighted in the article as well. This does not mean that traditional methods for predicting and preventing floods have become outdated. It simply means that newer technology can help strengthen existing ones and aid in saving lives. The information that can be captured by the Artificial Intelligence (AI) can benefit the community in leveraging and building the community resources. There is a need to give equal importance to rehabilitation of affected people post the disaster. The authors have also highlighted how modern-day technology can help provide human services such as counselling to survivors during and after floods.

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Flood Prediction Using Hybrid ANFIS-ACO Model: A Case Study

Cited by 25 publications

References 27 publications

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

Forecasting of Flood Flow of Panam River Basin using Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN with Comparative Study

Reviewing the use of technology to manage floods in India

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