Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model

Reshma, T.; Reddy, K. Venkata; Pratap, Deva; Agilan, V.

doi:10.1007/s11269-017-1884-2

Cited by 16 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In finer time resolution flood assessments, the constraint is increasingly noticeable. Despite some examples of using a recurrent neural network (RNN) for hydrological modeling (Reshma et al 2018), there is no long short-term memory (LSTM) model that forecasts future river flow solely based on precipitation inputs to address the issue of accumulative uncertainty in the literature. Therefore, to overcome the model errors, the infiltration rate with the soil moisture and steep slopes in the hill is a vital part to calculate in the runoff model for the prediction of daily rainfall.…”

Section: Introductionmentioning

confidence: 99%

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Kumar

Pandey

Subalakshmi

et al. 2022

Journal of Hydroinformatics

View full text Add to dashboard Cite

Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Kumar

Pandey

Subalakshmi

et al. 2022

Journal of Hydroinformatics

View full text Add to dashboard Cite

show abstract

“…The problem of unequally spaced antenna array synthesis involves non-linear and non-convex optimization using a set of classical gradient-based algorithms deployed in nature-inspired optimization techniques. Several natureinspired optimization techniques, namely genetic algorithm (GA) [2]- [6], differential evolution (DE) [7]- [12], particle swarm optimization (PSO) [13]- [22], ant colony optimization (ACO) [23]- [25], cat swarm optimization [26]- [32], grey wolf optimization (GWO) [33]- [34], and bee colony optimization (BCO) [35]- [36] have been implemented while synthetizing unequally spaced antenna arrays. Some of the important algorithms relied upon in antenna array synthesis have been discussed in detail in the literature.…”

Section: Introductionmentioning

confidence: 99%

Unequally Spaced Antenna Array Synthesis Using Accelerating Gaussian Mutated Cat Swarm Optimization

Kumar¹,

Pappula²,

Madhav³

et al. 2022

JTIT

View full text Add to dashboard Cite

Low peak sidelobe level (PSLL) and antenna arrays with high directivity are needed nowadays for reliable wireless communication systems. Controlling the PSLL is a major issue in designing effective antenna array systems. In this paper, a nature inspired technique, namely accelerating Gaussian mutated cat swarm optimization (AGMCSO) that attributes global search abilities, is proposed to control PSLL in the radiation pattern. In AGM-SCO, Gaussian mutation with an acceleration parameter is used in the position-updated equation, which allows the algorithm to search in a systematic way to prevent premature convergence and to enhance the speed of convergence. Experiments concerning several benchmark multimodal problems have been conducted and the obtained results illustrate that AGMCSO shows excellent performance concerning evolutionary speed and accuracy. To validate the overall efficacy of the algorithm, a sensitivity analysis was performed for different AGMCSO parameters. AGMCSO was researched on numerous linear, unequally spaced antenna arrays and the results show that in terms of generating low PSLL with a narrow first null beamwidth (FNBW), AGMCSO outperforms conventional algorithms.

show abstract

“…One of the frequently used artificial intelligence-based technique is the artificial neural network (ANN) and have been successfully used by the various researchers for prediction of the water flow [27][28][29]. The ANN techniques are put in application successfully in numerous interdisciplinary domains [15,[18][19].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction

Yadav¹,

Prasad²,

Mojjada³

et al. 2020

RIA

View full text Add to dashboard Cite

In hydrology and water resource engineering, water flow forecasting is of great importance for getting the information about the river engineering, dam structure design and waterrelated inflow demand management. In order to prevent flooding on the downstream side of the river during the rainy season, sufficient outflow from a barrage should be maintained. It is very difficult to predict the desired water flow using physically-based models and conventional regression-based methods due to the nonlinear and fuzzy nature of hydrological activity and scarcity of relevant data. These traditional methods are incapable to handle the complex non-linearity and non-stationarity process of water flow. Thus, the aim of this study is to develop intelligent hybrid artificial intelligence model, namely genetic algorithm based Artificial Neural Network (GA-ANN) for monthly Water Flow prediction in the Mahanadi river system. All parameters associated with the artificial neural network (ANN) model are optimized simultaneous automatically using Genetic Algorithm (GA) for prediction of the Water flow. Twenty years monthly data from Mahanadi river in India has collected for the development of this GA-ANN model. The hydro-climatical parameters like Rainfall, Water Level, Sediment yield and Temperature are used for the development of the ANN prediction model of Water Flow at Tikarapara gauging station which is extreme last downstream station in Mahanadi River basin, India. The performances of the GA-ANN model were compared with Artificial Neural Network (ANN) model for checking the estimation capability of the model. The obtained results revealed that the proposed novel GA-ANN model is capable to predict river flow with satisfactory performances and provided better results than the ANN model. This modelling approach can be potentially used for prediction of water flow discharge in the river system where measurement of water flow is unavailable.

show abstract

Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model

Cited by 16 publications

References 29 publications

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm

Unequally Spaced Antenna Array Synthesis Using Accelerating Gaussian Mutated Cat Swarm Optimization

Application of Artificial Neural Network and Genetic Algorithm Based Artificial Neural Network Models for River Flow Prediction

Contact Info

Product

Resources

About