Mohammad Reza Ekhtesasi; mohammad sefid; Ali Morovati Sharif Abadi
Abstract
Zonation of quality of drinking water sources is necessary for Water Management in Arid and Hyper Arid areas. In this research, spatial variations of quality characteristics of water (EC, Na, K, Mg, Ca, NO3, SO4, Alkalinity, Cl, TH and TDS were investigated in one-year period (2015-2016) based on the ...
Read More
Zonation of quality of drinking water sources is necessary for Water Management in Arid and Hyper Arid areas. In this research, spatial variations of quality characteristics of water (EC, Na, K, Mg, Ca, NO3, SO4, Alkalinity, Cl, TH and TDS were investigated in one-year period (2015-2016) based on the data collected from 55 drinking deep wells of Yazd Plain. In order to evaluate and select the best method of interpolation was used Simple kriging, Ordinary kriging and Inverse Distance Weighted and RMSE. Zoning maps showed that groundwater of Charkhab and Yazdgerd respectively are in the Southeast and Northwest of study area, which are affected by flow of the Mehriz and Taft river, that are higher quality than the middle part of study area.The changes in the main indicators of quality such as TDS are from less than 500 to more than 2500 mg/lit and EC from less than 700 to more than 3,500 micromhos/cm that this achievement reflects the need for utilization management and preparation of drinking water with best quality to enter the network. Also the wells in the center of study area such as 16 Silo, 12 Dehno, 13 Dehno and 24 Dehno in terms of quality parameters such as TDS, TH, EC and Cl have more than double the national standard limit, that is recommended to remove them or adjustment operation of wells in drinking water network.
Hamide Afkhami; mohammad dastorani; farzaneh fotouhi firuzabadi
Abstract
Due to the nature of the sediment data, selection of appropriate methods for processing the data before entering them to the artificial intelligence models can enhance the reliability of simulations results. In this study, the effects of sediment data processing procedures on ANN and ANFIS models outputs ...
Read More
Due to the nature of the sediment data, selection of appropriate methods for processing the data before entering them to the artificial intelligence models can enhance the reliability of simulations results. In this study, the effects of sediment data processing procedures on ANN and ANFIS models outputs in 7 Dez Basin stations were evaluated. Accordingly, three scenarios were considered: In the first scenario, original data was used without exerting any processing technique; in the second scenario, the data was normalized; and in the third scenario, logarithm of data were used according to logarithmic distribution governing. The simulation results showed that using data logarithm leads to higher performance and lower error, especially in stations where the best fit probability distribution is one of the log family distributions. Finally, among applied models, ANFIS showed the best performance with coefficient efficiency of 0.95 and RMSE of 5.4, MSE of 1.4 and ME of 0.42 in Biatoon gauging station and using the third scenario.
