Artificial Neural Networks Method for Prediction of Rainfall-Runoff Relation: Regional Practice

Üneş, Fatih; Keskin, Levent; Demirci, Mustafa

doi:10.28978/nesciences.646198

Cited by 4 publications

(1 citation statement)

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“…There is no limit to the number of hidden or intermediate layers and the outputs of neurons in one layer can be presented to the next layer as input values employing weights. In the input layer, a weight coefficient is applied to obtained information with the help of the input vector; from here it is transmitted to the neurons in the hidden layer (Gümüş et al, 2018;Üneş et al, 2019). Afterwards, the output of the network is completed as a result of applying different processes to the information in the hidden and output layers (Fig.…”

Section: ; Gümüş 2019)mentioning

confidence: 99%

A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall–Runoff Relationship in Water Resources Management

Turhan¹

2021

J. Ecol. Eng.

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Recently, Artificial Neural Network (ANN) methods, which have been successfully applied in many fields, have been considered for a large number of reliable streamflow estimation and modeling studies for the design and project planning of hydraulic structures. The present study aimed to model the rainfall-runoff relationship using different ANN methods. The Nergizlik Dam, located in the Seyhan sub-basin and one of the important basins in Turkey, was chosen as the study area. Analyses were carried out based on streamflow estimation with the help of observed precipitation and runoff data at certain time intervals. Feed Forward Backpropagation Neural Network (FFBPNN) and Generalized Regression Neural Network (GRNN) methods were adopted, and obtained results were compared with Multiple Linear Regression (MLR) method, which is accepted as the traditional method. Also, the models were performed using three different transfer functions to create optimum ANN modeling. As a result of the study, it was seen that ANN methods showed statistically good results in rainfall-runoff modeling, and the developed models can be successfully applied in the estimation of average monthly flows.

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Section: ; Gümüş 2019)mentioning

confidence: 99%

A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall–Runoff Relationship in Water Resources Management

Turhan¹

2021

J. Ecol. Eng.

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Prosumer in smart grids based on intelligent edge computing: A review on Artificial Intelligence Scheduling Techniques

Sami

2022

Ain Shams Engineering Journal

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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

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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.

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Artificial Neural Networks Method for Prediction of Rainfall-Runoff Relation: Regional Practice

Cited by 4 publications

References 25 publications

A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall–Runoff Relationship in Water Resources Management

A Comparative Evaluation of the Use of Artificial Neural Networks for Modeling the Rainfall–Runoff Relationship in Water Resources Management

Prosumer in smart grids based on intelligent edge computing: A review on Artificial Intelligence Scheduling Techniques

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

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