Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins a
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Artificial Neural Network and Fuzzy Inference System Models for Forecasting Suspended Sediment and Turbidity in Basins at Different Scales Laís Coelho Teixeira 1 & Priscila Pacheco Mariani 1 Nilza Maria dos Reis Castro 1 & Vanessa Sari 2
& Olavo Correa Pedrollo
1
&
Received: 5 February 2020 / Accepted: 6 August 2020/ # Springer Nature B.V. 2020
Abstract
The monitoring of hydro-sedimentological processes is important for environmental control but depends on resources that are not always available. The estimation of sedimentological variables with mathematical models is often limited by the scarcity of data for a single basin. This research experiments with the simulation of suspended sediment concentration (SSC) and turbidity (T) using a regional model, with data from agricultural basins of different scales within the same hydrographic region, using hourly precipitation as one of the predictive variables, aggregated through the exponentially weighted moving average (EWMA) of past rainfall, in artificial neural network (ANN) and fuzzy inference system (FIS) models. The data monitoring was performed from January 2013 to February 2020 in four watersheds within the same region in southern Brazil, with areas ranging from 1.3 to 524.3 km2. For the turbidity estimation, the FIS model, which also made use of the discharge (Q) and area (A) of each basin as inputs, performed best, with a Nash-Sutcliffe efficiency (NS) of 0.860 for the verification samples. Several FIS and ANN models performed very well for SSC prediction (with NSs ranging from 0.950 to 0.977) due to the EWMA variable, including an FIS model that uses only this variable (NS 0.952). The results allow us to conclude that it is possible, with few data for the individual basin and a regional empirical model, to estimate SSC and turbidity, provided the aggregation of hourly precipitation by the EWMA, as long as the basins have similar physical and climatic characteristics. Keywords Suspended sediment concentration . Artificial intelligence modelling . Nested hydrographic basins . Exponentially weighting moving averages of past precipitation
* Laís Coelho Teixeira [email protected]
1
Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
2
Universidade Federal de Santa Maria, Santa Maria, Brazil
Teixeira L.C. et al.
1 Introduction Suspended sediments account for approximately 70 to 90% (Morgan 2005) of the annual river sediment yield, influencing aquatic environments (Harrison et al. 2007) and landscapes (Dean et al. 2016), interfering with hydraulic structures (Choubin et al. 2018), reservoirs and river morphology (Chen et al. 2018). Suspended sediments are considered one of the major pollutants of water resources because they carry pollutants adsorbed to particles, such as nutrients, pesticides and other chemical components (Afan et al. 2015). These sediments are consequences of erosion in the watersheds caused by the presence of erosive agents (climate, anthropic action and characteristics of the
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