*Editorial* **Statement of Peer Review †**

**Athanasios Loukas**

> Department of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; agloukas@topo.auth.gr

> † Presented at the 7th International Electronic Conference on Water Sciences, 15–30 March 2023; Available online: https://ecws-7.sciforum.net/.

> In submitting conference proceedings to *Environment Sciences Proceedings*, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal.


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**Citation:** Loukas, A. Statement of Peer Review. *Environ. Sci. Proc.* **2023**, *25*, 103. https://doi.org/10.3390/ environsciproc2023025103

Published: 1 June 2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

### *Proceeding Paper* **Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India †**

**Usman Mohseni \* and Sai Bargav Muskula**

† Presented at the 7th International Electronic Conference on Water Sciences, 15–30 March 2023; Available online: https://ecws-7.sciforum.net/.

**Abstract:** The present study examines the rainfall-runoff-based model development by using artificial neural networks (ANNs) models in the Yerli sub-catchment of the upper Tapi basin for a period of 36 years, i.e., from 1981 to 2016. The created ANN models were capable of establishing the correlation between input and output data sets. The rainfall and runoff models that were built have been calibrated and validated. For predicting runoff, Feed-Forward Back Propagation Neural Network (FFBPNN) and Cascade Forward Back Propagation Neural Network (CFBPNN) models are used. To evaluate the efficacy of the model, various measures such as mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R) are employed. With MSE, RMSE, and R values of 0.4982, 0.7056, and 0.96213, respectively, FFBPNN outperforms two networks with model architectures of 6-4-1 and Transig transfer function. Additionally, in this study, the Levenberg– Marquardt (LM), Bayesian Regularization (BR), and Conjugate Gradient Scaled (CGS) algorithms are used to train the ANN rainfall-runoff models. The results show that LM creates the most accurate model. It performs better than BR and CGS. The best model is the LM-trained method with 30 neurons, which has MSE values of 0.7279, RMSE values of 0.8531, and R values of 0.95057. It is concluded that the constructed neural network model was capable of quite accurately predicting runoff for the Yerli sub-catchment.

**Keywords:** artificial neural network; rainfall-runoff modeling; feed-forward back propagation; cascade forward back propagation
