Vahid Gholami

Bio: Vahid Gholami is an academic researcher from University of Gilan. The author has contributed to research in topics: Surface runoff & Groundwater. The author has an hindex of 14, co-authored 47 publications receiving 616 citations. Previous affiliations of Vahid Gholami include Islamic Azad University.

Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data

Abstract: Soil erosion and sediment transport measurement is a time-consuming and difficult step yet important part of hydrological studies. Hence, use of models has become commonplace in estimating soil erosion and sediment transport. In this study, we used an artificial neural network (ANN) to simulate soil erosion rates. A geographic information system (GIS) was used as a pre-processor and post-processor tool to present the spatial variation of the soil erosion rate. The ANN was trained, optimized and verified using data from the Kasilian watershed located in the northern part of Iran. Field plots were used to estimate soil erosion values on the hillslopes. A Multi Layer Perceptron (MLP) network was adopted, where the soil erosion rate was the output variable and the rainfall intensity and amount, air and soil temperature, soil moisture, vegetation cover and slope were the inputs. After the training process, the network was tested. According to the test results, the ANN can estimate soil erosion with an acceptable level (coefficient of determination = 0.94, mean squared error = 0.04). The verified network and its inputs were used to estimate soil erosion rates on the hillslopes. Finally, a soil erosion rate map was generated based on the results of the verified network and GIS capabilities. The results confirm the high potential when coupling an ANN and a GIS in soil erosion estimation and mapping on the hillslopes.

Efficiency of the geomorphologic instantaneous unit hydrograph method in flood hydrograph simulation

Abstract: Several methods and models to simulate rainfall-runoff processes have been presented, and each of them has its own advantages and disadvantages. In the present study, different methods were applied to simulate the rainfall–runoff process over the Kasilian Watershed located in northern Iran, including Snyder, SCS, Trianglar, Rosso and Geomorphoclimatic unit hydrographs. The study was intended to compare the accuracy and reliability of a geomorphologic model with Snyder, SCS, Trianglar, Rosso and Geomorphoclimatic Unit hydrographs. In addition, this study attempted to determine the shape and dimensions of outlet runoff hydrographs in a 68.8 km 2 area in the Kasilian Basin, which is located in the Mazandaran Province of Iran. The first twenty-one equivalent rainfall–runoff events were selected, and a hydrograph of outlet runoff was calculated for each. The peak time and peak flow of outlet runoff in the models were then compared, and the model that most efficiently estimated hydrograph of outlet flow for similar regions was determined. The comparison of calculated and observed hydrographs showed that the geomorphologic model had the most direct agreement for the parameters of peak time and peak flow of direct runoff. Statistical analyses of the models demonstrated that the geomorphological model had the smallest main relative and square error. The study's results confirm the high efficiency of the Geomorphoclimatic Unit Hydrograph and its ability to increase simulation accuracy for runoff and hydrographs.

Global patterns of groundwater table depth.

Abstract: Shallow groundwater affects terrestrial ecosystems by sustaining river base-flow and root-zone soil water in the absence of rain, but little is known about the global patterns of water table depth and where it provides vital support for land ecosystems We present global observations of water table depth compiled from government archives and literature, and fill in data gaps and infer patterns and processes using a groundwater model forced by modern climate, terrain, and sea level Patterns in water table depth explain patterns in wetlands at the global scale and vegetation gradients at regional and local scales Overall, shallow groundwater influences 22 to 32% of global land area, including ~15% as groundwater-fed surface water features and 7 to 17% with the water table or its capillary fringe within plant rooting depths

Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran

Abstract: Evaporation accounts for varying shares of water balance under different climatic conditions, and its correct prediction poses a significant challenge before water resources management in watershed...

Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

Abstract: A drought forecasting model is a practical tool for drought-risk management. Drought models are used to forecast drought indices (DIs) that quantify drought by its onset, termination, and subsequent properties such as the severity, duration, and peak intensity in order to monitor and evaluate the impacts of future drought. In this study, a wavelet-based drought model using the extreme learning machine (W-ELM) algorithm where the input data are first screened through the wavelet pre-processing technique for better accuracy is developed to forecast the monthly effective DI (EDI). The EDI is an intensive index that considers water accumulation with a weighting function applied to rainfall data with the passage of time in order to analyze the drought-risk. Determined by the autocorrelation function (ACF) and partial ACFs, the lagged EDI signals for the current and past months are used as significant inputs for 1 month lead-time EDI forecasting. For drought model development, 97 years of data for three hydrological stations (Bathurst Agricultural, Wilsons Promontory and Merredin in Australia) are partitioned in approximately 90:5:5 ratios for training, cross-validation and test purposes, respectively. The discrete wavelet transformation (DWT) is applied to the predictor datasets to decompose inputs into their time–frequency components that capture important information on periodicities. DWT sub-series are used to develop new EDI sub-series as inputs for the W-ELM model. The forecasting capability of W-ELM is benchmarked with ELM, artificial neural network (ANN), least squares support vector regression (LSSVR) and their wavelet-equivalent (W-ANN, W-LSSVR) models. Statistical metrics based on agreement between the forecasted and observed EDI, including the coefficient of determination, Willmott’s index, Nash–Sutcliffe coefficient, percentage peak deviation, root-mean-square error, mean absolute error, and model execution time are used to assess the effectiveness of the models. The results demonstrate enhanced forecast skill of the drought models that use wavelet pre-processing of the predictor dataset. Based on statistical measures, W-ELM outperformed traditional ELM, LSSVR, ANN and their wavelet-equivalent counterparts (W-ANN, W-LSSVR). It is found that the W-ELM model is computationally efficient as shown by a faster running time with the majority of forecasting errors in lower frequency bands. The results demonstrate the usefulness of W-ELM over W-ANN and W-LSSVR models and the benefits of wavelet transformation of input data to improve the performance of drought forecasting models.