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 3 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%

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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Unleashing the power of AI: revolutionizing runoff prediction beyond NRCS-CN method

Tarate,

Raut

2024

Arab J Geosci

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An artificial neural network and SCS–CN-based model for runoff estimation: a case study of the Peddavagu watershed

Raj,

Kumar,

Aishwarya

et al. 2024

Water Practice &Amp; Technology

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The accurate prediction of runoff from rainfall events is crucial for effective water resource management, especially in regions with diverse climatic patterns like India. This study proposes a novel approach by integrating the soil conservation service (SCS)–curve number (CN) method with artificial neural networks (ANNs) to model rainfall–runoff relationships. In this research, an SCS–CN method is utilized to estimate initial runoff volumes, accounting for local soil and land-cover characteristics. Subsequently, an ANN-based model is developed to capture the complex nonlinear relationships between rainfall inputs and runoff outputs. Taking the rainfall of the watershed as the inputs, an ANN model was developed in MATLAB for runoff simulation at the main outlet of the Peddavagu watershed. A feed-forward back-propagation method is employed in the ANN model. The architecture with a 1-10-1 configuration based on tan–sig transfer function performed well in terms of MSE 0.019 and R2 values, which were 0.96 and 0.98 for the training and testing phases, respectively. The model efficiency was satisfactory with the coefficient of correlation (R), 0.96 for training, 0.98 for validation, and 0.98 for testing period. The overall value of R (0.96) indicates the utility of this ANN–SCS-based coupled model for rainfall–runoff simulation.

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

Cited by 3 publications

References 13 publications

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

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

Unleashing the power of AI: revolutionizing runoff prediction beyond NRCS-CN method

An artificial neural network and SCS–CN-based model for runoff estimation: a case study of the Peddavagu watershed

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