Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model

Abstract

a b s t r a c tUrmia lake basin located in northwestern Iran is the second largest saline lake in the world. Due to many reasons i.e. climate changes, several dam constructions, building a bridge across the Lake, extra agricultural consumption and improper management of water resources, the water level of the lake has been decreased since 1997 and thousand hectares of emerged salty land has made numerous ecological and environmental problems. Therefore, an accurate forecast of the entrance runoff to the lake is important in managing the river flow and water transfer within basins. There are various methods for time-series based forecasting; in the presented study Feed-forward Neural Network and Autocorrelation Regressive Integrated Moving Average (ARIMA) models were applied to forecast the monthly rainfall in Urmia lake basin. The results showed that the estimated values of monthly rainfall through Feed-forward NN were close to ARIMA model with coefficient of correlation 0.62 and the root mean square error of 12.43 mm over the 6 years test period; then rainfall amount were predicted for a 6-year period starting from

Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in-situ data for calibrating a hydrological model

“…Precipitation reduction, temperature increase, agricultural development including construction of man-made dams and building a causeway across the lake have been identified as the main reasons for the degradation of Lake Urmia (Abbaspour and Nazaridoust, 2007;Zeinoddini et al, 2009;Delju et al, 2012;Jalili et al, 2012;Sima and Tajrishy, 2013;Fathian et al, 2014;Farajzadeh et al, 2014;Banihabib et al, 2015;AghaKouchak et al, 2015;Azarnivand et al, 2015;Alizadeh-Choobari et al, 15 2016;Ghale et al, 2018). Using Gravity Recovery And Climate Experiment (GRACE) satellite observations, altimetry data for Lake Urmia and outputs of the Global Land Data Assimilation System (GLDAS), Forootan et al (2014) estimated the trend of groundwater storage changes in the Lake Urmia basin as -11.2 mm/yr between the years of 2005 to 2011, the largest decrease of the six investigated Iranian basins.…”

A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

“…The major challenge lies in choosing the proper number of the neurons in the hidden layer. Furthermore, it must be considered that each input is first multiplied by the corresponding weight parameter and the resulting product is added to a bias to produce a weighted sum [21]. The resulted weighted sum of each neuron passes through a neuron activation function (i.e., transfer function) to produce the final output of the neuron.…”

Spatial clustering of maximum 24‐h rainfall over Urmia Lake Basin by new weighting approaches

“…The range of elevation, according to the digital elevation model (DEM), varies from 1114 to 3758 m above sea level throughout the ULB (Figure 1). In addition, the elevation of the lake's body of water and the neighbouring plains varies above 1114 and below 2000 m. The annual mean rainfall based on recorded daily rainfall is 352 mm during 1973-2011 (Farajzadeh et al, 2014). Figure 2 shows an isohyetal map of maximum amounts of MR over the ULB.…”