The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge.The daily data of 12 years (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

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Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine with linear and radial basis kernel functions (SVM-LF and SVM-RF), were employed for daily discharge (Q) estimation. The hydrological data of daily stage (H) and discharge (Q) from June to October for 10 years (2004–2013) at the Govindpur station, situated in the Burhabalang river basin, Orissa, were considered for analysis. For model construction, an optimum number of inputs (lags) was extracted using the partial autocorrelation function (PACF) at a 5% level of significance. The outcomes of the WANN, SVM-LF, and SVM-RF models were appraised over the observed value of Q based on performance indicators, viz., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (PCC), and Willmott index (WI), and through visual inspection (time variation, scatter plot, and Taylor diagram). Results of the evaluation showed that the SVM-RF model (RMSE = 104.426 m3/s, NSE = 0.925, PCC = 0.964, WI = 0.979) outperformed the WANN and SVM-LF models with the combination of three inputs, i.e., current stage, one-day antecedent stage, and discharge, during the testing period. In addition, the SVM-RF model was found to be more reliable and robust than the other models and having important implications for water resources management at the study site.

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988–1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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