In this study, data classification method was evaluated to increase accuracy of estimating suspended sediment load. To achieve this objective, suspended sediment in Chehelgazi and Khalife Tarkhan rivers in Kurdistan, Iran, were estimated using sediment rating curve (SRC) method in three different approaches of data classification. At first, measured data were modelled without classification. Then, data based on flow statues were divided into two series as high and low flow. Eventually, based on sediment concentration, the data were divided into low and high sediment concentration. Long-term runoff and sediment data were used to calibrate rating curve model. The estimated values were compared with recorded data and the performances of these models were evaluated using statistical criteria. The results indicated an effective role of data classification to improve estimating sediment transportation by rating curve method. In one of the stations, it was observed that due to classification based on river flow and sediment concentration, model efficiency was increased about 45% and 28%, respectively. Furthermore, in case of improving efficiency of SRC method, classifying data based on flow statues was found to be more effective than sediment concentration. The results of this study can be used to improve the management of the watershed by more accurately estimating the amount of suspended sediments transporting in the rivers draining to reservoirs.
Replacing irrigated with rainfed crops and sustainable production of major rainfed plants (such as wheat) can be an efficient strategy to restore water resources that are drying up. Identifying plant response to climate is essential to advancing this strategy and planning for precision agriculture. Wheat is the main plant of Saqez in the Lake Urmia basin of Iran, whose yield is associated with severe fluctuations. This study was conducted to investigate the climate effect on wheat yield fluctuation. For this purpose, the method of growing degree days (GDDs) and the Zadoks scale were used to divide the wheat growth period into seven stages. Forty-seven climatic variables of the first six stages were used to do factor analysis and to develop the model for forecasting pre-harvest yield. Gene expression programming (GEP), artificial neural networks (ANNs), and multivariate linear regression (MLR) methods were applied to develop the model. The results showed that 90.7% of the total variance of 47 variables can be explained by 10 factors. Eighty-two percent of yield variations were modeled by these 10 factors (r = 0.91). The mean absolute percentage error (MAPE) for the models developed by the GEP and ANN methods was 26%, and its amount for the MLR model was 35%. In this study, for the first time, the GEP method was used to model rainfed wheat yield. Comparison with MLR and ANN methods shows that GEP is suitable for modeling in this field.
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