ArticleEnvironmental science and pollution research international2018
Using the combined model of gamma test and neuro-fuzzy system for modeling and estimating lead bonds in reservoir sediments.
Article in Environmental science and pollution research international, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Source identification, accumulation and dispersion of heavy metals pollution into the surface sediments of urban runoff (case study, channels of Tehran City).Journal of environmental health science & engineering · 2025Article
- Assessment of environmental and health risks of potentially toxic elements associated with desert dust particles affected by industrial activities in Isfahan metropolitan.Scientific reports · 2024Article
- Article
- Assessing the carcinogenic and non-carcinogenic health risks of metals in the drinking water of Isfahan, Iran.Scientific reports · 2024Article
- Potentially toxic elements contamination in the water resources: an integrated risk assessment approach in the upper Citarum watershed area.Environmental geochemistry and health · 2024Article
- Prediction of human health risk and disability-adjusted life years induced by heavy metals exposure through drinking water in Fars Province, Iran.Scientific reports · 2023Article
- Removal of toxic lead from aqueous solution using a low-cost adsorbent.Scientific reports · 2023Article
- Heavy metal contaminated soil, water, and vegetables in northeastern Iran: potential health risk factors.Journal of environmental health science & engineering · 2022Article
- Heavy metal concentrations and health risk assessment in urban soils of Neyshabur, Iran.Environmental monitoring and assessment · 2022Article
- Groundwater Pollution and Human Health Risks in an Industrialized Region of Southern India: Impacts of the COVID-19 Lockdown and the Monsoon Seasonal Cycles.Archives of environmental contamination and toxicology · 2021Article
- Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms.Data in brief · 2020Article
- The effects of geochemical processes on groundwater chemistry and the health risks associated with fluoride intake in a semi-arid region of South India.RSC advances · 2020Article
- Chemical data on environmental matrices from an abandoned mining site.Data in brief · 2019Article
- Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis.MethodsX · 2019Article
- Carcinogenic and non-carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran.MethodsX · 2019Article
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5 authors.
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Abstract
Heavy metals attract a great deal of attention nowadays due to their potential accumulation in living creatures and transference in the food chain. Sediments of water reservoirs are considered to be a source of accumulation of these metals that develop in response to human activities and soil erosion. This study collected 180 samples of the surface sediments of water reservoir 1 at Chahnimeh in Sistan. Efficiency of the ANFIS model was evaluated to estimate the five bonds following the measurement of parameters in the laboratory.The following results were obtained for the parameters: organic carbon (OC) %, 0.31; cation exchange capacity (CEC), 37.07 Cmol kg; total Pb, 25.19 mg/kg; clay %, 45.87; and silt %, 39.02. These parameters were used as input for the training model. In the output layer, lead bonds were chosen as modeling targets in the following way: Pb f1 (4.61); Pb f2 (0.54); Pb f3 (16.28); Pb f4 (3.42); and Pb f5 (0.38) mg/kg. The best input compound in this model was chosen using the gamma test. From a total of 180, 88 data were considered for the model training section. Eventually, the neural-fuzzy model (subtractive clustering), developed for the prediction of lead bonds in the studied region, was able to account for over 99% of lead bonds in the sediments; considering statistical criteria of root mean squares error or RMSE (0.0337-0.0813) and determination coefficient or R
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