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Showing 2 results for Zamanzad-Ghavidel
Evaluation of the Effects of Morphological, Hydrological, and Land-Use Characteristics on River Water Abstraction Using Data Mining Methods
A.r. Emadi, S. Fazeli, M. Hooshmand, S. Zamanzad-Ghavidel, R. Sobhani,
Volume 27, Issue 1 (Spring 2023)
Abstract
The agricultural sector as one of the most important sectors of water consumption has great importance for the sustainability of the country's water resources systems. The objective of this study was to estimate the river water abstraction (RWA) for agricultural consumption in the study area of Nobaran in the Namak Lake basin. The RWA was estimated using variables related to morphological, hydrological, and land use factors, as well as a combination of their variables collected through field sampling. Data mining methods such as adaptive-network-based fuzzy inference systems (ANFIS), group method of data handling (GMDH), radial basis function (RBF), and regression trees (Rtree) were also used to estimate the RWA variables. In the current study, the GMDH24 model with a combined scenario including the variables of river width, river depth, minimum flow, maximum flow, average flow, crop, and the garden cultivated area was adopted as the best model to estimate the RWA variable. The RMSE value for the combined scenario of the GMDH24 model was found to be 0.046 for estimating RWA in the Nobaran study area. The results showed that the performance of the GMDH24 model for estimating RWA for maximum values is very acceptable and promising. Therefore, modeling and identifying various variables that affect the optimal RWA rate for agricultural purposes fulfills the objectives of integrated water resources management (IWRM).

Temporal and Spatial Estimation of Quality Variables of Groundwater Resources Based on Intelligent Methods
A.r. Emadi, R. Fazloula, S. Zamanzad-Ghavidel, R. Sobhani4, S. Nosrat-Akhtar,
Volume 27, Issue 3 (Fall 2023)
Abstract
As one of the most necessary human needs, groundwater resources play a key role in the economic and political processes of societies. Climatic and land-use changes made serious challenges to the quantity and quality of groundwater resources in the Tehran-Karaj study area. The main objective of the present study is to develop a method based on individual intelligent models, including adaptive neural-fuzzy inference system (ANFIS), gene expression programming (GEP), and combined-wavelet (WANFIS, WGEP) methods for temporal and spatial estimation of total hardness (TH), total dissolved solids (TDS), and electrical conductivity (EC) variables in the groundwater resources of the Tehran-Karaj area for statistical period of 17 years (2004-2021). The results showed that 
combined-wavelet models have higher performance than individual models in estimating three selected variables. So that the performance improvement percentage of the WANFIS model compared to ANFIS and WGEP model compared to GEP, taking into account the evaluation index of root mean square error (RMSE) were obtained (23.713%, 18.018%), (12.581%, 33.116%), and (6.433%, 12.995%) for TH, TDS, and EC variables, respectively. The results indicated a very high spatial and temporal compatibility of the estimated values of the WGEP model with the observed values for all three qualitative variables in the Tehran-Karaj area. The results showed that the concentration of qualitative variables of groundwater resources from the north to the south of the study area has an upward trend for all three qualitative variables. In urban areas, pollution caused by sewage and population increase, as well as in agricultural areas, the use of chemical fertilizers and their continued infiltration into groundwater resources and 
over-extraction of groundwater resources aggravate their pollution. Therefore, in the study area, climatic changes and the type of land use are strongly related to the quality of groundwater resources.
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