## 1 Introduction  
Through basin management, different strategies have been applied to decrease sediment volume. Sedimentation processes and soil erosion are important in the fields of hydrology and hydraulics [1]. Sediment load relies on rainfall and runoff, as these hydrological variables can change the sedimentation process. Sedimentation can decrease crop yield, available water volume, and soil quality [2]. Sediment load can also lead to pollution of the environment due to the transport of pollutants; thus, it is necessary to predict sediment load. Additionally, understanding the sedimentation process is important for the protection of hydraulic and flood structures [3]. Accurate prediction and estimation of sediment load is necessary for water resource management, flood control, water quality, and so on. [4]. In addition, other hydrological variables have a significant impact on sediment load prediction. Thus, application of accurate prediction models is necessary for the planning and management of basins. Soft computing models have been widely applied to simulate hydrological variables [5]. These models are applied for forecasting the climate variables in different fields. By using soft computing models, model accuracy can be enhanced [6]. Soft computing models can present conceptions of the hydrological process but need a large set of inputs. With soft computing models, outputs are obtained without in-depth observation of the physical details of the hydrological issues being investigated. A key benefit of soft computing models is that they can learn the system behavior when hydrological inputs are inserted into the model. The other benefits of applying soft computing models are fast computation, high accuracy, high flexibility, and a simple computation process. The investigated literature reviews emphasize that the preparation of soft computing models has unknown parameters [7; 8; 9]. These studies show that the training of adaptive neuro fuzzy system (ANFIS) parameters is an important challenge during the simulation process. Although there are some training algorithms, such as gradient methods, for obtaining ANFIS parameters, the computation of gradients in each level is very sophisticated. In addition, training of artificial neural network (ANN) model parameters is a main challenge. For example, the number of neurons in hidden layer(s), weight values, and biases are important parameters for the ANN models [10].  
An effective optimization algorithm is necessary for improving soft computing models. There are different soft computing models, but high skill is required to determine the unknown parameters of such models in order to improve their performance. An accurate investigation is also necessary to select the best architecture of ANFIS and multilayer feed-forward neural network (MFNN) models [11]. It should be mentioned that although the ANFIS and MFNN models have high performances among other simulation models, their accuracies depend on accurate preparation of their parameters. Thus, new optimization algorithms can be applied for updating and training of the ANFIS and ANN parameters [12]. Thus, different studies attempted to use new training strategies that are more accurate and easier than the traditional methods for obtaining model parameters.  
Developing efficient models for suspended load prediction is the important objective of this article. The different steps of this article are as follows:  
1. To improve ANFIS and MFNN model efficiency by applying new optimization algorithms. These algorithms are used to obtain the best ANFIS and MFNN structures and parameters;
2. To predict monthly sediment load by applying improved ANFIS and MFNN models;
3. To examine the uncertainty of the predictions; and
4. To obtain a sediment map of the case study.