Runoff Prediction Using Artificial Neural Network and SCS-CN Method: A Case Study of Mayurakshi River Catchment, India

Mandal, Subhadeep; Biswas, Sujata

doi:10.1007/978-981-15-9805-0_4

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Especially after the successful discovery of various applications (pattern recognition, signal processing, and forecasting) that can be solved with backpropagation which makes ANN more attractive to people [16], [8]. Forecasting and simulation of hydro-climatology data, including rainfall, humidity, temperature, solar irradiation, and wind speed have been carried out using ANN backpropagation in various regions [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Comparison of coastal wind speed in Southeast Asian Countries using ANN backpropagation algorithm

Syaharuddin,

Muharani,

Ibrahim

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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This study aims to analyze the neural network backpropagation (ANN-BP) algorithm in seeing the development and comparison of wind speed. We estimated the wind speed of coastal areas with a case study of five countries in Southeast Asia. We used data for the last 10 years in the form of monthly data. The ANN-BP architecture uses input layers, three hidden layers, and one output layer with 12-25-10-1 architecture. From the simulation results in the training stage, we obtained information that the data model that is closest or follows the wind speed pattern with the smallest MSE value is the coastal area in Thailand with an MSE value of 0.00012 in the training stage and 0.00011 in the testing stage. Then, we also obtained that the average prediction of wind speed on the coast of Brunei Darusslam obtained the smallest value compared to other countries.

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Section: Introductionmentioning

confidence: 99%