# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
###### Abstract
It has been widely adopted for hydro-meteorological estimation such as solar radiation assessment [23; 24; 25], streamflow assessment [26; 27] reservoir water level estimation [28] and SSL assessment [29]. The ANN technique mimics the biological brain and nervous system functioning with a self-learning capability. ANN has great potential in the modelling of complex and non-linear features of the hydro-meteorological time series. It is a black-box approach with no need for prior knowledge that has been applied to develop an efficient link between inputs and outputs. An extensive review of ANN applications in the hydrological field for the estimation and prediction of numerous hydrological parameters has been acknowledged by the ASCE Task Committee [30; 31]. In the last two decades, studies have shown that artificial neural networks (ANNs) have promising results in terms of modelling and forecasting streamflow [32; 33], reservoir water level [34; 35], and suspended sediment in rivers [2; 36; 37; 38]. The ANN model is especially employed where basic physical interactions are not entirely known, but there are sufficient data to train a network. [PERSON] et al. [39] have compared various conventional methods and soft computing approaches for the prediction of suspended sediment load. [PERSON] and [PERSON] [40] compared different soft-computing techniques, such as Gene Expression Programming (GEP), ANFIS and ANNs, for a daily forecast of SSC. [PERSON] et al. [41] compared the performance of various conventional sediment-rating curves (SRCs) and soft-computing approaches such as ANFIS and ANNs to estimate the daily SSL. [PERSON] et al. [42] modelled suspended sediment using four different heuristic techniques and two regression-based techniques. [PERSON] et al. [43] investigated the extrapolation performance of the ANN models for suspended sediment data and concluded that the ANN model provides a closer estimation of the observed peaks than conventional models. [PERSON] et al. [1] predicted the missing and future daily suspended sediment load with the ANN model for the Mangla reservoir, Pakistan, and used it as a boundary condition for the hydraulic model to calculate the bed level changes and delta advancement rate for greater precision. In recent years, the wavelet transform (WT) analysis has become a powerful tool because it simultaneously interprets the temporal and frequency information of the signal, which improves the model performance. The wavelet transform analysis gives a time-frequency representation of a signal at various periods in the time domain, and it also analyzes the non-stationarities in time series [44]. Previous studies have also revealed that the WT analysis tool surpassed the Fourier transform in investigations of the non-stationary time series [45]. The WT analysis is a comparatively innovative approach in the field of water resource research, which can create different time series that classify probable trends, seasonal deviations, and internal correlation among different variables. Recently, numerous studies have reported on the employment of coupled wavelet transform and ANN (WANN) models to investigate and forecast the different parameters of hydrological and environmental engineering. Further, many former studies have indicated that the proficiency of short- or long-term forecasts of the hydrological data is enhanced by employing the hybrid WANN model approach owing to the advantage of multiresolution sub-time series as the ANN input data [2,46]. [PERSON] et al. [47] developed a hybrid WANN model for monthly rainfall-runoff modelling on the Bhakhara River, Uttarakhand. [PERSON] et al. [48] enhanced ANN prediction accuracy in the context of the daily flow of intermittent rivers by employing WANN models. [PERSON] et al. [2] estimated the SSL by using different soft computing approaches and concluded that the performance of the ANN model was enhanced by coupling WT analysis. [PERSON] et al.