Mapping Historical Water-Supply Qanat Based On Fuzzy Method. An Application to the Isfahan Qanat (Isfahan, Iran)

Behzadi, Saeed; Mousavi, Zahra; Norouzi, E.

doi:10.29252/nmce.3.4.24

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…agriculture and mining). Chronic water shortages, with an annual average rainfall of less than 100 mm in this region have led to increasing changes in the hadrochemical parameters in the groundwater (Behzadi et al 2019;Shara et al 2023). However, most of the studied sites showed the values for the water quality within the permissible range of WHO and USEPA.…”

Section: Physicochemical Characteristics Of Groundwatermentioning

confidence: 99%

Does Nickel-contaminated groundwater in a mining area cause health risks to vulnerable consumers?

Salehi,

Esmaeilbeigi,

Kazemi

et al. 2023

Preprint

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This study examined the health risk associated with nickel (Ni) in the groundwater of South Khorasan, Eastern Iran. A total of 110 stations were included in the study, comprising 62 wells, 40 qanats, and 8 springs in the summer of 2020. Initially, the collected samples were tested for temperature, pH, and electrical conductivity (EC). Subsequently, the samples were filtered and treated with nitric acid (HNO3) to measure the concentration of Ni using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Hazard quotient (HQ) and non-carcinogenic risk assessments were employed to evaluate the potential risks of Ni to the inhabitants. The findings revealed that the concentration of Ni ranged from 0.02 to 132.39 µg l− 1, and only two stations exhibited Ni concentrations above the WHO standards (20 µg l− 1). The results demonstrated that 98.21% of the sampled locations had HQ values below one, indicating negligible risk, while 1.78% of the stations exhibited HQ values of one or higher, representing a high non-carcinogenic risk for water consumers. Overall, the concentration of nickel in the groundwater of South Khorasan exceeded the World Health Organization (WHO) limit solely in the Halvan station, posing a non-carcinogenic risk for the residents in that area, and therefore, additional efforts should be made to provide healthier groundwater to consumers in this region.

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Section: Physicochemical Characteristics Of Groundwatermentioning

confidence: 99%

Does Nickel-contaminated groundwater in a mining area cause health risks to vulnerable consumers?

Salehi,

Esmaeilbeigi,

Kazemi

et al. 2023

Preprint

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show abstract

“…By using standard deviation and correlation statistics, parameters that have greater correlation and amplitude than the others are determined, and then a specific number of optimal parameters is used instead of all the parameters and executed according to formula (2), in this study. This formula is set for three parameters, but in this research, it is applied for five parameters (including four bands of satellite imagery and surface evaporation values):…”

Section: Determining Optimal Bands For Relative Humidity and Temperaturementioning

confidence: 99%

“…Therefore, using remote sensing data to model climate data is a convenient and economical way. In recent years, the artificial intelligence approaches such as Coactive Neuro-Fuzzy Inference System (CANFIS), Adaptive Neuro-Fuzzy Inference System (ANFIS) which is hybrid of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS), Fuzzy-Logic (FL) [2], Radial Basis Neural Network (RBNN) which is a type of ANN, Support Vector Machines (SVM), Generalized Regression Neural Network (GRNN) which is a type of ANN, Genetic Algorithm (GA), Wavelet Transformation (WT) and Multi-Layer Perceptron Neural Network (MLPNN) have been significantly utilized in diverse fields such as modelling climate indicators [3][4][5][6][7][8][9][10][11][12][13][14][15]. In order to model the target data, researchers used terrestrial station data as inputs, which have many limitations.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning methods and satellite images to estimate combined climatic indices

Norouzi

Behzadi

2019

NMCE

Self Cite

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The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role in modeling and data mining of climatic phenomena as a result of their significant advantages, including their availability and analysis. Therefore, addressing the improvement and expansion of machine learning methods and modeling algorithms using remote sensing data is inevitable. In this study, 7 well-known machine learning algorithms are applied with different initial data to show that satellite images are able to estimate the combined indices more accurately. A new index (HT) is also defined by combining the quantities of relative humidity and temperature. Then, machine learning algorithms are trained for each of these three quantities. For each of the temperature and relative humidity quantities, four optimal bands were selected using the PCA method, then a combination of these optimal bands was determined for the HT index. Two criteria are used to validate the results: Root Mean Square Error (RMSE) statistic and comparing the map of the interpolation method with the result of this study. RMSE values show that satellite imagery could have a high ability to model and estimate composite indices. Classification-KNN-Coarse and Ensemble-Bagged Trees with accuracy of 79.8626 % and 84.9281% are identified as the best machine learning methods for temperature and relative humidity, while the best accuracy to estimate the HT index is 92.8792% for Matern 5/2 GPR. Therefore, it can be said that by changing the methods of database preparation, the modeling results can be changed effectively in order to train models.

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“…Shanghai traffic flow data was used to guide the study process, which includes data processing, cluster analysis and methodological comparisons. The results also showed that the Fuzzy C-Means (FCM) clustering algorithm is improved in both fuzzy membership and weight degree (Behzadi, Mousavi, & Norouzi, 2019;Cheng et al, 2020;Mahjoobi & Behzadi, 2022). In Qasim, Ziboon, and Falih (2018) conducted a study to analyze Transcad and GIS techniques to evaluate the transportation network in Nasiriyah city, Deir ez-Zor province.…”

Section: Introductionmentioning

confidence: 99%

Predicting Traffic Data in GIS using Different Neural Network Methods

Ghasempoor

Behzadi

2022

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Traffic is one of the most influential factors in choosing the route to reach the destination. It can be said that a large percentage of people prefer a long but low traffic route than a short route with heavy traffic. Therefore, traffic is a very determining factor in societies, especially in metropolitan areas. The issue of traffic forecasting is another important factor in the field of traffic. In such a way that the traffic of the coming days can be predicted based on the traffic of the previous days. In this paper, traffic forecasting in the coming days is done using a neural network algorithm based on the collected traffic data. Traffic forecasting is performed using Basic Neural Network methods, Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network. The results of the forecast are then compared with real observations. The results show that the Feed-forward Levenberg-Marquardt method predicts traffic data with 81.59% accuracy, which is the most accurate method among the others. The accuracy of Bayesian Neural Network, Conjugate Gradient Neural Network and Basic Neural Network methods is 81.55, 81.50 and 75%, respectively. Regression values of 24 hours a day were also estimated and it was found that the proximity of input and output values in the Basic Neural Network method is approximately 80%. This parameter was obtained 69.69%, 69.71% and 69.87% for three Feed-forward Levenberg-Marquardt, Conjugate Gradient Neural Network and Bayesian Neural Network respectively.

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Mapping Historical Water-Supply Qanat Based On Fuzzy Method. An Application to the Isfahan Qanat (Isfahan, Iran)

Cited by 8 publications

References 20 publications

Does Nickel-contaminated groundwater in a mining area cause health risks to vulnerable consumers?

Does Nickel-contaminated groundwater in a mining area cause health risks to vulnerable consumers?

Evaluating machine learning methods and satellite images to estimate combined climatic indices

Predicting Traffic Data in GIS using Different Neural Network Methods

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