Groundwater
Sana Maleki; Vahid Nourani; Hessam Najafi
Abstract
Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces ...
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Systems for assessing groundwater vulnerability are designed to protect groundwater resources from pollution. The DRASTIC method is a well-known approach for determining groundwater susceptibility. One drawback of the DRASTIC method is that it relies on expert judgment to rank parameters, which introduces uncertainty. This study used a new generation of Fuzzy Logic (FL), called the Z-number theory, to estimate the specific vulnerability of aquifers and address this uncertainty. The specific vulnerability of the Ardabil and Qorveh-Dehgolan aquifers was estimated using two scenarios: the DRASTIC parameters as inputs and nitrate concentration values as output. The vulnerability of the aquifer was also evaluated by comparing the results of the proposed models with those of the DRASTIC model, which served as a benchmark. The analysis showed that the Z-number Based Modeling (ZBM), which considered data reliability and weighted the rules appropriately, produced higher-quality results than the classic FL. In the Ardabil plain, the ZBM yielded results that were 53% better (using seven inputs) and 184% better (using four inputs) compared to the classic FL. In the Qorveh-Dehgolan Plain (QDP), the ZBM produced results that were 127% better (using seven inputs) and 311% better (using four inputs) than the classic FL. The irregularity and non-linearity of the data, such as the high coefficient of variation (CV) in the Ardabil plain compared to the QDP, may contribute to the high CV value in the plains. Therefore, in plains with high CV, the quality of the extracted Z-number-based rules may be lower.
Mehdi Hayatzadeh; Sahar Amini; Ali Fathzadeh; Maryam Asadi
Abolghasem Amir Ahmadi
Volume 4, Issue 12 , December 2017, , Pages 131-152
Abstract
Extent Abstract
Introduction
Gully erosion is a major problem for natural resource management, leading to land degradation and economic losses worldwide. Determining the threshold for research on Geomorphology and natural ecosystems is important for many scholars. Land managers and specialists knowledge ...
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Extent Abstract
Introduction
Gully erosion is a major problem for natural resource management, leading to land degradation and economic losses worldwide. Determining the threshold for research on Geomorphology and natural ecosystems is important for many scholars. Land managers and specialists knowledge about factors affecting the growth of gully enables them to control them and predict their growth rate under similar conditions in other ecosystems. In the study area, this type of erosion has caused many lands to be destroyed, and with runoff and flood runoff, there is a significant amount of sediment that leads to unutilized land. It seems that examining these factors and determining their thresholds will help determine control strategies and more successful implementation of water and soil conservation projects. The purpose of this study was to determine the threshold of effective factors in the longitudinal growth of gullies using data mining techniques in Sanganeh Kalat watershed in the northern part of Khorasan Razavi province.
Methodology
Initially, the location of 23 gullies was recorded using the Global Positioning System (Garmin 76CSX) and the distribution map of the gullies in the study area. Then, the Soil gravel, bare soil, cover, litter in heads of the selected gully were measured.
For this purpose, 15 plots were placed in one square meter and their means and the previously mentioned parameters were determined. In order to measure the physical and chemical properties of the soil, a soil sample was taken from a point at the head of each gully. After they were transferred to the erosion and sedimentation laboratory, the electrical conductivity (ECe), PH, OM, SAR, Clay, Silt and the Sand were measured. Also, the permeability at the top site of head of each gully was calculated using double cylinders. In addition, the amount of water penetration of the soil was calculated. Finally, using the data mining technique (K-Means Clustering and CART Decision Tree), the threshold of the factors influencing the longitudinal growth of gully in the study area was determined.
Discussion
Of the total of 23 gullies studied in this study, the accuracy of the estimation based on the parameters influencing the longitudinal extension of the gullies in the final model were measured and were respectively 100% and 85% for the educational and experimental data sets. The interpretation of the rules extracted from the decision tree of the CART, based on the clustering of the length of the gullies, is as follows:
- The results of the analysis of the CART decision tree algorithm show that when the width of the gullies is 275.32, the SAR is 0.147, the gullies headcuts slope is 1.39, and the percentage of silt would increase from 37.12, long-length gullies (cluster 1) are created.
- In the formation of mid-range gullies, when the ratio of girder width is greater than 198.84, the SAR is less than or equal to 0.174, and the gradient of the gully headcuts slope is less than 0.73, the average length gullies (cluster 2) are created.
- When the width of the gullies is from 108.77 m, the SAR is less than or equal to 0.174, and the gullies headcuts slope is smaller or equal to 0.481, gullies of low length (101.35 to 163.23 m) are created.
Conclusion
The results of the decision tree of CART based on the length of gullies clustering showed that the most important factors affecting the longitudinal expansion of gullies in the study area were gully width, SAR, gully headcuts slope and silt percentage.As a result, the main factor in the longitudinal expansion of gullies is the surface runoff. The second factor is the soil erosion sensitivity in the study area. The main reason for this is the poor vegetation and low soil permeability. In addition, the texture of the soil is another factor that overwhelms the longitudinal extension of the gullies. The prevalence of the amount of silt in the soil texture is due to the lack of adhesion, waste, and the transfer of more sediment, resulting in the longitudinal extension of the gullies.
Mohammadtaghi Sattari; Rasoul Mirabbai Najafabadi; Masood Alimohammadi
Volume 3, Issue 8 , December 2016, , Pages 73-92
Abstract
Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with ...
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Received: 2015.08.16 Accepted: 2016.11.18 Mohammadtaghi Sattari[1]* Rasoul Mirabbasi Najafabadi[2] Masood Alimohammadi[3] Abstract Accurate prediction of droughts in arid and semi-arid countries, like Iran, have important role in water resources management and designing appropriate plans for coping with drought consequences. Since the standardized precipitation index (SPI) is known as a suitable index for drought analysis, in this study, we used the M5 rule tree model for forecasting SPI values. For this purpose, the monthly precipitation data of Maragheh synoptic station were used during a 25-year period for calculating SPI values at 6-month time scale (SPI-6). The results indicated that the Maragheh region was faced with successive and severe droughts in recent two decays. In the next step, the SPI-6 values were forecasted for next 1 to 12 months using M5 rule tree model. The results showed that the SPI-6 values in previous time steps had the most effect on forecasting the next SPI-6 values, and the forecasting accuracy decreases with increasing prediction length. So the correlation coefficient of forecasting SPI-6 for next month was obtained 0.94 which this value was decreased to about 0.40 for forecasting SPI-6 for next 12 months. However, the M5 rule tree model provides more understandable, applicable and simple linear relation in forecasting droughts and shows relatively good performance and accuracy. [1]- Assistant Professor, Department of Water Engineering, University of Tabriz (Corresponding Autor), Email:mail:mtsattar@gmail.com. [2]- Assistant Professor, Department of Water Engineering, Shahrekord University. [3]- MSc of Civil Engineering.
