Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India †
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. Methodology
2.2.1. Following Steps Should Be Performed for Developing an ANN Model Using NNTOOL
- Data Collection: The required observed data (rainfall, runoff, temperature, specific humidity, surface pressure, wind speed) at the prerequisite station are to be collected.
- Import Data: The collected data are imported into the NNTOOL box as input and target data.
- Creating Network: The network is created by selecting a suitable network type, i.e., FFBPNN or CFBPNN. The network architecture is formed (6-2-1, 6-3-1, 6-4-1).
- Number of Neurons: For the given network, the number of neurons is taken as 10 or 20.
- Network Training: The developed network is trained based on performance function.
- Result: Once the network is trained, the result is checked by plotting the regression plot, and the predicted output is obtained.
- Retraining: If the obtained regression plot is not satisfactory, then reinitialization of weights has to be conducted by changing the number of neurons.
- Model Evaluation: Based on statistical parameters such as (MSE), (RMSE), (R2), and (R).
2.2.2. Following Steps Should Be Performed for Developing an ANN Model Using NNSTART
- Neural Fitting App: The Neural Fitting app will help to select data, create and train a network, and evaluate its performance using mean square error and regression analysis.
- Data Selection: The collected data will be used as both the input and output data. The input data are in a 6 × 36 matrix. On the other hand, the target data are in a 1 × 36 matrix.
- Validation and Test: The data are split as follows, 70% (training), 15% (validation), and 15% (testing).
- Network Architecture: For the given network, the number of neurons is taken as 10, 20, and 30.
- Select Algorithm: For training, the algorithms, namely Levenberg–Marquardt (trainlm), Bayesian Regularization (trainbr), and Scaled Conjugate Gradient (trainscg), were used.
- Train Network: To fit the input and goal data, train the network.
- Retrain: The network is retrained if a satisfactory regression plot is not obtained.
- Output: Desired predicted output is obtained after fixing the regression plot.
2.3. Model Evaluation Criteria
- Mean Square Error (MSE):
- Root Mean Square Error (RMSE):
- Regression Coefficient (R): Using Regression Plot between predicted and observed runoff.
3. Results and Discussion
3.1. NNTOOL
3.1.1. Feed Forward Back Propagation Neural Network (FFBPNN)
3.1.2. Cascade Forward Back Propagation Neural Network (CFBPNN)
3.2. NNSTART
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Tokar, A.S.; Johnson, P.A. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 1999, 4, 232–239. [Google Scholar] [CrossRef]
- Namara, W.G.; Damise, T.A.; Tufa, F.G. Rainfall Runoff Modeling Using HEC-HMS: The Case of Awash Bello Sub-Catchment, Upper Awash Basin, Ethiopia. Int. J. Environ. 2020, 9, 68–86. [Google Scholar] [CrossRef] [Green Version]
- Rathod, P.; Borse, K.; Manekar, V.L. Simulation of rainfall-runoff process using HEC-HMS (case study: Tapi river, India). In Proceedings of the 20th International Conference on Hydraulics, Water Resources and River Engineering, Roorkee, India, 17–19 December 2015. [Google Scholar]
- Chen, J.; Adams, B.J. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J. Hydrol. 2006, 318, 232–249. [Google Scholar] [CrossRef]
- Zhang, B.; Govindaraju, R.S. Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resour. Res. 2000, 36, 753–762. [Google Scholar] [CrossRef]
- Lu, P.; Chen, S.; Zheng, Y. Artificial intelligence in civil engineering. Math. Probl. Eng. 2012, 2012, 145974. [Google Scholar] [CrossRef] [Green Version]
- Tayyab, M.; Zhou, J.; Adnan, R.; Zeng, X. Application of Artificial Intelligence Method Coupled with Discrete Wavelet Transform Method. Procedia Comput. Sci. 2017, 107, 212–217. [Google Scholar] [CrossRef]
- Chae, Y.T.; Horesh, R.; Hwang, Y.; Lee, Y.M. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 2016, 111, 184–194. [Google Scholar] [CrossRef]
- Khayatian, F.; Sarto, L.; Dall’O’, G. Application of neural networks for evaluating energy performance certificates of residential buildings. Energy Build. 2016, 125, 45–54. [Google Scholar] [CrossRef]
- Kisi, O.; Shiri, J.; Tombul, M. Modeling rainfall-runoff process using soft computing techniques. Comput. Geosci. 2013, 51, 108–117. [Google Scholar] [CrossRef]
- Rogers, L.L.; Dowla, F.U. Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling has been successfully applied to a variety of optimization. Water Resour. Res. 1994, 30, 457–481. [Google Scholar] [CrossRef]
- Vandana, M.; John, S.E.; Maya, K.; Sunny, S.; Padmalal, D. Environmental impact assessment (EIA) of hard rock quarrying in a tropical river basin—Study from the SW India. Environ. Monit. Assess. 2020, 192, 580. [Google Scholar] [CrossRef] [PubMed]
- Sahour, H.; Gholami, V.; Vazifedan, M. A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. J. Hydrol. 2020, 591, 125321. [Google Scholar] [CrossRef]
- Orimi, M.G.; Farid, A.; Amiri, R.; Imani, K. Cprecip parameter for checking snow entry for forecasting weekly discharge of the Haraz River flow by artificial neural network. Water Resour. 2015, 42, 607–615. [Google Scholar] [CrossRef]
- Chandwani, V.; Vyas, S.K.; Agrawal, V.; Sharma, G. Soft Computing Approach for Rainfall-runoff Modelling: A Review. Aquat. Procedia 2015, 4, 1054–1061. [Google Scholar] [CrossRef]
- Chang, T.K.; Talei, A.; Quek, C.; Pauwels, V.R.N. Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure. J. Hydrol. 2018, 564, 1179–1193. [Google Scholar] [CrossRef]
- Samantaray, S.; Sahoo, A. Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study. Int. J. Knowl.-Based Intell. Eng. Syst. 2020, 24, 243–251. [Google Scholar] [CrossRef]
Data Type | Data Source |
---|---|
Digital Elevation Model | USGS Earth Explorer |
Rainfall | Central Water Commission |
Meteorological data | India Meteorological Department |
Discharge | Central Water Commission |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. 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/).
Share and Cite
Mohseni, U.; Muskula, S.B. Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India. Environ. Sci. Proc. 2023, 25, 1. https://doi.org/10.3390/ECWS-7-14232
Mohseni U, Muskula SB. Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India. Environmental Sciences Proceedings. 2023; 25(1):1. https://doi.org/10.3390/ECWS-7-14232
Chicago/Turabian StyleMohseni, Usman, and Sai Bargav Muskula. 2023. "Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India" Environmental Sciences Proceedings 25, no. 1: 1. https://doi.org/10.3390/ECWS-7-14232
APA StyleMohseni, U., & Muskula, S. B. (2023). Rainfall-Runoff Modeling Using Artificial Neural Network—A Case Study of Purna Sub-Catchment of Upper Tapi Basin, India. Environmental Sciences Proceedings, 25(1), 1. https://doi.org/10.3390/ECWS-7-14232