Using the combined model of gamma test and neuro-fuzzy system for modeling and estimating lead bonds in reservoir sediments

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 (0.92-0.99), this model showed good performance with regard to prediction.

Carcinogenic and non-carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran

“…Many heavy metals are the natural elements of the earth’s crust. Weathering and decomposition of metal rock and ores can transfer heavy metals in groundwater and have led to human exposure for the entire history of mankind [5,13,14]. The levels of metals vary significantly from the soil of one region to another [15].…”

Protocol for the estimation of drinking water quality index (DWQI) in water resources: Artificial neural network (ANFIS) and Arc-Gis

“…Adaptive network-based fuzzy inference (ANFIS), based on the first-order Sugeno fuzzy model, was used in this study [29]. This method combines multilayer feed forward back-propagation network and fuzzy inference system and takes the advantages of artificial neural networks and fuzzy logic [30,31].…”

Chemical data on environmental matrices from an abandoned mining site

“…Environmental and health problems [2], [3], [4], [5], [6], [7] associated with the dispersion of metals and with their transfer from the geosphere to the biosphere [8], [9], [10], [11] are becoming increasingly common worldwide. The knowledge of metal pathways is a fundamental parameter to plan efficient remediation actions of waters and soils and it can be achieved by an accurate geochemical investigation.…”