Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge

Abstract

The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, or Nash Sutcliff 's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.

A review of the adaptability of hydrological models for drought forecasting

“…The MIKE-SHE model covers ranges of physical processes with high requirements on parameters and data, and hence its operation seems more sophisticated than the other three above-mentioned distributed models (Refsgaard and Storm, 1995). The SWAT model is an open source model and it is updated by world researchers, which have developed several modules for human impacts and crop growth.…”

“…There are two main types of methods involved in the hydrological drought forecasting. One is the statistical method that tries to develop construct the relationship between hydrological characteristics and drought events, such as the gray forecasting method (Vishnu and Syamala, 2012), Markov chain method (Paulo and Pereira, 2007), Error back Propagation neural net-work (Raju et al, 2011), and correlation analysis. The second method is the methods based on hydrological models and coupled atmospheric-hydrological models (Mishra and Singh, 2011).…”

Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm

“…The processes functions and parameters of the ANN are dependent on the nature of the problem [44]. Hence, there is no universal design for the ANN that is applicable for any (or even a certain) type of experimental data [49]. Consequently, approaching the optimum design for the ANN is mainly a trial and error procedure [45] and also on the examples and experiences provided by the user [53].…”

Biogeomorphology of salt marshes

“…droughts) (Hughes et al, 2012;Kirwan and Megonigal, 2013;Chapple and Dronova, 2017). An artificial neural network (ANN) can be trained (French et al, 1992;Raju et al, 2011;Bomers et al, 2019c), for example to estimate the salt marsh width by using long-term datasets of these parameters and aerial imagery to determine the salt marsh width, without using a complex process-based model. Previously, salt marshes have been characterized with such techniques (Morris et al, 2005).…”

“…Moreover, once there is a general understanding of the parameters driving salt marsh development, including the width of the salt marsh, data-driven techniques can be used to predict the salt marsh width. For example regression models or artificial neural networks can be used (French et al, 1992;Morris et al, 2005;Raju et al, 2011). Moreover, the potential of using these techniques will increase, due to the increasing availability of continues measurements and aerial imagery.…”