Mashhad Plain Research Articles

Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS.

Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.

Sustainability and determining the optimal population based on water resources in Mashhad, Iran

This study first evaluates Mashhad's water resource sustainability, employing sustainable urban development models proposed by Haughton. Second, it determines the optimal number of population in 2016, both with and without the Dousti and Ardak dams. Results show that the Mashhad plain possesses only 2% of the total province groundwater although 65.97% of the urban provincial population live there. Furthermore, 92.7% of surface water comes from the Dousti dam, which is located 220 km from Mashhad, and only 30% of domestic sewage is recycled. Using Haughton's theory of urban metabolism, Mashhad is thus not a sustainable city, as, including the Dousti and Ardak as water sources (dependence out of hinterland), it has a surplus population of 550,459 and excluding them, a surplus of 1,192,660 people.

Lithogenic and anthropogenic pollution assessment of Ni, Zn and Pb in surface soils of Mashhad plain, northeastern Iran

Mashhad in northeastern Iran, is a fast-growing metropolitan city which has been faced with a high rate of urbanization and industrial activities during recent decades. Despite the high geological diversity of Mashhad plain, the effect of anthropogenic and lithogenic factors on concentrations of potentially toxic elements (PTEs) in the soil have not yet been documented. The objectives of this study were to investigate spatial distribution, anthropogenic and lithogenic origins, and soil pollution intensity of Ni, Zn and Pb in Mashhad plain. A total of 178 soil samples (0–10cm) were collected across an area of about 1500km2. The aqua-regia extractable concentrations of selected PTEs were determined. Results showed concentration for Ni, Zn, and Pb ranged from 33.3–415.8, 35.4–149, and 19.6–69.1mgkg−1 with mean values of 61.4, 74.1, and 31.6mgkg−1, respectively. Calculated background (median+2MAD) concentrations of Ni, Zn and Pb were 60.1, 78 and 28.6mgkg−1, respectively. Pb concentration in 70% of total soil samples was greater than background value; as for Ni and Zn, this figure was 16% and 35%, respectively. The highest concentration of Ni was found in soils formed on ultramafic rocks, whereas the highest concentrations of Zn and Pb occurred in the urban cities of Mashhad and Chenaran. Maximum pollution indices of Ni, Zn and Pb were 6.39, 1.91, and 2.42 with mean values of 0.94, 0.95, and 1.11, respectively. Spatial distribution maps indicated high concentrations of Pb and Zn in Mashhad and Chenaran. The concentration of these elements measured highest in the center of the city. Ni concentration decreased as the distance from ultramafic rocks increased. Results indicated that Ni concentration in the studied soils was controlled by parent material whereas increasing concentrations of Pb and Zn were under the influence of anthropogenic sources. The spread of urbanization and industrial activities in recent decades seem to be responsible for the contamination of soils by Pb and Zn. Although PI values revealed that the soil was not highly polluted, certain strategies must be considered to prevent further pollution in the future.

Impacts of geology and land use on magnetic susceptibility and selected heavy metals in surface soils of Mashhad plain, northeastern Iran

Magnetic susceptibility is a fast, inexpensive and reliable technique for estimating and monitoring the anthropogenic contamination of soil with heavy metals. However, it is essential to determine the factors affecting magnetic susceptibility before applying this technique to environmental studies. The objectives of this study were to investigate i) the effect of parent materials and land use on the magnetic susceptibility and concentrations of Fe, Ni, Pb and Zn, and ii) capability of magnetic susceptibility as an indicator of anthropogenic heavy metals contamination of soil in Mashhad plain, northeastern Iran. One hundred seventy-eight composite surface soil samples (0–10cm) were taken. The aqua-regia extractable concentrations of Fe, Ni, Zn and Pb were determined by atomic absorption spectroscopy. Magnetic susceptibility at low and high frequency (χlf and χhf) were measured and frequency dependent susceptibility (χfd) was calculated. The average concentrations of Fe, Ni, Pb and Zn were 22,812, 61.4, 74.1 and 31.6mg kg−1, respectively. The highest contents of Pb (69.1mg kg−1) and Zn (149mg kg−1) were observed in urban area. The highest concentration of Ni was 41,538mg kg−1 observed in the soils developed from ultramafic rocks. Magnetic susceptibility varied from 20.3 on marly sediments to 311.8×10−8m3kg−1 on ultramafic rocks. A positive strong correlation (Pvalue<0.01, r=0.88) was obtained between Ni and χlf. There were no significant relationships between Zn and Pb with χlf, therefore it seems that magnetic susceptibility has not been affected significantly by anthropogenic activities which enhanced Pb and Zn concentrations in urban soils. The results indicated that magnetic susceptibility was mainly controlled by Ni containing minerals with lithogenic origin. Therefore, in the soils studied, magnetic susceptibility could not be employed as indicator of anthropogenic contamination of soil with heavy metals.

GIS-based Groundwater Spring Potential Mapping Using Data Mining Boosted Regression Tree and Probabilistic Frequency Ratio Models in Iran

This study intends to investigate the performance of boosted regression tree (BRT) and frequency ratio (FR) models in groundwater potential mapping. For this purpose, location of the springs was determined in the western parts of the Mashhad Plain using national reports and field surveys. In addition, thirteen groundwater conditioning factors were prepared and mapped for the modelling process. Those factor maps are: slope degree, slope aspect, altitude, plan curvature, profile curvature, slope length, topographic wetness index, distance from faults, distance from rivers, river density, fault density, land use, and lithology. Then, frequency ratio and boosted regression tree models were applied and groundwater potential maps (GPMs) were produced. In the last step, validation of the models was carried out implementing receiver operating characteristics (ROC) curve. According to the results, BRT had area under curve of ROC (AUC-ROC) of 87.2%, while it was seen that FR had AUC-ROC of 83.2% that implies acceptable operation of the models. According to the results of this study, topographic wetness index was the most important factor, followed by altitude, and distance from rivers. On the other hand, aspect, and plan curvature were seen to be the least important factors. The methodology implemented in this study could be used for other basins with similar conditions to cope with water resources problem.

Investigating Suitable Areas for Flood Spreading Using Fuzzy Logic and Analytic Network Process (ANP) (Case Study: Mashhad Plain)

Investigating Suitable Areas for Flood Spreading Using Fuzzy Logic and Analytic Network Process (ANP) (Case Study: Mashhad Plain)

The Modeling of Precipitation and Future Droughts of Mashhad Plain using Stochastic Time Series and Standardized Precipitation Index (SPI)

The purpose of this study is to model precipitation characteristics and simulation of drought using the Standardized Precipitation Index (SPI) during 2011-2020 in Mashhad station, Iran. To this end, first, the data related to the average of monthly precipitation in synoptic station of Mashhad (from 1951 to 2010) were obtained from the Meteorological Organization of Iran. Using the method proposed by Box-Jenkins, the monthly precipitation was modeled in 2011 to 2020, with respect to its preceding series trend. In addition, using SPI, climatic conditions in the upcoming years were investigated in terms of drought. The results indicated that the seasonal-multiplicative statistical model of SARIMA (2, 0, 1) (2, 1, 1)12 is a good technique for fitting the precipitation data. Using this model, the pattern of monthly precipitation in Mashhad station from 2011 to 2020 was modeled. The results revealed that the average of monthly precipitation in the next 10 years will decrease about 26mm compared with the last 10 years (2001-2010). The correlation of precipitation in the upcoming 10 years compared with the previous 10 years is about 96%. The SPI suggested that in a 6-month timescale, in the upcoming 10 years, Mashhad station will face drought about 18%; in a 12-month timescale it would be about 17%.

Modeling of available soil phosphorus (ASP) using multi-objective group method of data handling

The accurate estimation of available soil phosphorus (ASP) is required for the effective management of environmental resources and improving agricultural productivity. Direct measurement of ASP at large scales is time-consuming and expensive. Therefore, many empirical models or pedotransfer functions (PTFs) have been developed to obtain ASP. This study introduces a new model that estimates ASP from easily measurable soil properties by using the multi-objective group method of data handling (mGMDH) approach. Organic carbon, clay content, calcium carbonate equivalent, pH and ASP were obtained from 100 soil samples, which were collected by the stratified random sampling strategy in Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. The suggested model for the prediction of ASP generated satisfactory results in terms of the statistical performance criteria. The coefficient of determination (R2) and mean square error (MSE) values of the mGMDH model in training step were 0.606 and 4.9671 mg kg−1, respectively, whereas those were 0.504 and 5.6685 mg kg−1, in the testing step. It was concluded that only 50 % of variations of ASP could be described by selecting easy-to-measure soil variables and owing to the key role of phosphorus in soil fertility and precision agriculture, the other related factors such as terrain attributes and fertilizer management should also be considered as they influence the distribution of the soil phosphorus.

Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models

Salinization and alkalization of land resources are the major obstacles to their optimal usage in many arid and semi-arid regions of the world, including Iran, since potential evapotranspiration is more noteworthy than precipitation in these areas. The amount of water that enters the soil is low and this results in salt accumulation in soils, which makes the soil infertile. Moreover, existence of salts, for example, sodium, in soils causes dispersion of soil particles and soil degradation, and intensifies soil erosion too. Monitoring exchangeable sodium percentage (ESP) variability in soils is both time-consuming and costly. However, in order to estimate the amounts of amendments and land management, it is necessary to know ESP variation and values in sodic or saline and sodic soils. Thus, introducing a method, which utilizes easily obtained indices to estimate ESP indirectly is more optimized and economical. Input and output data, i.e., ECe (dS m−1), clay (%), pH and ESP (%) were collected and measured from 100 soil samples in light of a stratified random sampling from Mashhad Plain, Khorasan-e-Razavi Province, Northeast Iran. This study aims to propose some models to estimate ESP by easily obtained properties of soil. In this regard, the efficiency of artificial intelligence-based (AI) models (i.e., Artificial Neural Network, ANN, and Adaptive Neuro-Fuzzy Inference System, ANFIS) was investigated and compared. Accuracy results showed that owing to highest R2 and the lowest mean square error (MSE), ANFIS model predictions were superior to the MLP model for indirect estimation of soil exchangeable sodium percentage.

Assessment of land capability for different irrigation systems by parametric and fuzzy approaches in the Mashhad Plain, northeast Iran

Bagherzadeh A., Paymard P . (2015): Assessment of land capability for different irrigation systems by parametric and fuzzy approaches in the Mashhad Plain, northeast Iran. Soil & Water Res., 10: 90–98. Water quality and quantity in agricultural systems of arid and semi- arid regions of the world are of great im portance. In this regard the trend to pressurized irrigation systems compared to surface irrigation, elevating water use efficiency, has drastically increased in the agriculture sector. The present study aimed to assess land capability for different types of irrigation systems including surface, drip, and sprinkler practices by parametric and fuzzy approaches to evaluate the capability of cultivated lands on 6131 km 2 of the Mashhad Plain, Khorasan Razavi Province, northeast Iran. In this regard land qualities (drainage and slope), soil physical and chemical properties (texture, depth, salinity, drainage, calcium carbonate and gypsum percentage) and climate condi tions (wind velocity) were evaluated by using the Geographic Information System (GIS). Based on parametric approach, some 1116.5 ha of the study area were classified as highly suitable (S1 class) for surface irrigation, while the corresponding values by fuzzy approach accounted for 6099.7 ha of the region. The moderately suitable class of S2, assessed by parametric and fuzzy approaches, included 5014.5 and 31.3 ha of the plain, respectively. It was revealed that the land capability indices were in higher classes (S1 to S2) by drip and sprinkler irrigation compared to the surface irrigation system and the soil texture was detected as the most limiting factor for using the surface irrigation system. With respect to current soil and climate conditions in the study area, the most efficient irrigation systems are drip and sprinkler practices.

Coupling wavelet transform with time series models to estimate groundwater level

Modeling and forecasting groundwater level is useful to develop managerial activities. This study was carried out to evaluate the wavelet-time series models to estimate groundwater levels. First, three time series models, i.e., autoregressive moving average (ARMA), autoregressive integrated moving average model (ARIMA), and SARIMA were used in their single form to predict groundwater level for different time steps in two sub-basins in Mashhad plain, Iran. In the next step, these models were combined with transformation. Results showed that the single models do not have a strong ability to cope with the non linearity and seasonality behavior of data. However, combination of these models with wavelet transformation could improve groundwater level modeling. Results showed that wavelet-SARIMA hybrid model had the best performance. In addition, it is concluded that all modeling approaches are satisfactory only to forecast groundwater level for 1 and 2 months ahead. This study also showed that these models forecasted groundwater level for 1 and 2 months ahead accurately but their accuracy decreases for 3 and 4 months ahead.

Evaluation of ANFIS, ANN, and geostatistical models to spatial distribution of groundwater quality (case study: Mashhad plain in Iran)

Groundwater is one of the major sources of exploitation in arid and semiarid regions. Spatial and temporal quality distribution is an important factor in groundwater management. Thus for protecting groundwater quality, data on spatial and temporal distribution are important. Geostatistical models are the most advanced techniques for interpolation and spatial prediction of groundwater parameters. Determining the best and the most suitable model is also very essential which is the main aim in this study. In this research, inverse distance weighted (IDW), kriging, and cokriging methods in geostatistical and artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models were used for predicting the spatial distribution of groundwater electrical conductivity (EC) and those were compared together. EC and chloride (Cl−) are two important indicators for water quality assessment. Data were related to 120 wells in Mashhad plain (Iran). Groundwater resources have an important role in this region due to surface water deficit. After normalization of data, to geostatistical models, variograme was drawn; for selecting a suitable model for fitness on experimental variograme, less RSS value was used. Then using cross-validation and root mean square error (RMSE), the best method for interpolation was selected. To compare these three models, we used 25 % of observation data and determined the R2, RMSE, and MAE parameters. Different ANFIS structures were examined. Also in ANFIS method, different types of membership function, such as Gaussian, bell shape, and trapezoid for inputs of model, were used. Results showed that for interpolation of groundwater quality, cokriging method is superior to kriging method in geostatistical model. In cokriging method, Cl parameter was selected as auxiliary variable which had the highest correlation with EC. Results showed that ANN model had the best accuracy (R2 = 0.932, RMSE = 367.9, MAE = 265.78 μmos/cm) than ANFIS and geostatistical models.

Estimation of soil losses by USLE model using GIS at Mashhad plain, Northeast of Iran

The Universal Soil Loss Equation (USLE) is an erosion estimation model to assess the soil losses that would generally result from splash, sheet, and rill erosion. At the present study, spatial distribution of different erosion prone areas were identified by USLE model to determine the average annual soil losses at Mashhad plain, northeast of Iran. Soil losses were estimated on a 100 × 100 m cell basis resolution by overlaying the five digital parameter layers (R, K, LS, C, P). To determine the critical soil loss regions at the plain, cell-based USLE parameters were multiplied by Arc-GIS ver.9.3. The estimated annual soil losses values were subsequently grouped into five classes ranging from 0 to 0.25 t/h/year around the trough line of the plain at Kashaf-rud River to 2–10 t/ha/year at the hills and pediment plains. Our results indicated a good correlation between land units of hills and pediment plains with the values of soil losses at the study area (R2 = 0.72), also the statistical analysis exhibited a high correlation between land use/cover of dry farming and soil losses (R2 = 0.78).

Agro-ecological zoning for wheat (Triticum aestivum), sugar beet (Beta vulgaris) and corn (Zea mays) on the Mashhad plain, Khorasan Razavi province

Climate is the most important factor determining the sustainability of agricultural production systems. A qualitative land evaluation was carried out for the Mashhad plain, Khorasan Razavi province, Iran, to assess the suitability of the land to grow the locally most important crops, i.e. wheat (Triticum aestivum), sugar beet (Beta vulgaris) and corn (Zea mays) using a Geographical Information System (GIS). The possible growing seasons were defined as early (10 September–20 June) and late (10 October–20 July) season for wheat, early (15 March–15 October) and late (15 April–15 November) season for sugar beet, and early (1 May–1 November) and late (15 May–15 November) season for corn. The study area covered approximately 99.915ha−1. Climate variables were taken into account including maximum, optimum and minimum daily average temperatures and were obtained from 30years agro-meteorological data set from 12 synoptic stations. Growing Degree Days (GDDs) were determined for wheat, sugar beet, and corn crops from sowing to harvest. To produce digital elevation model for Mashhad plain two sources were used on utilization of the IRS III satellite images with resolution that is 23.5m, and topographic maps with scale of 1:25000. Aspect and slope layers were produced by Arc GIS 9.2 software. The study identified suitable elevation, slope, and GDDs for optimal growth and indicated that high yields are possible for wheat, sugar beet, and corn on the Mashhad plain. The study also identified the most suitable regions of the Mashhad plain for each crop.

Physical land suitability evaluation for specific cereal crops using GIS at Mashhad Plain, Northeast of Iran

Land evaluation is the process of predicting land use potential on the basis of its attributes. In the present study, the physical land suitability evaluation approach was investigated for specific cereal crops including irrigated wheat, barley, grain maize and sorghum, based on FAO land evaluation frameworks (FAO, 1976, 1983, 1985) and the proposed method by Sys et al. (1991) at Mashhad Plain, Northeast Iran. Twenty eight soil profiles were studied on seven land units by a precise soil survey and their morphological and physicochemical properties were determined. Climatic and land qualities/characteristics of four cereal crops were determined using the tables of crop requirements developed by Sys et al. (1993). An interpolation function was used to map values to scores in terms of land qualities/characteristics for land utilization types and the evaluation was carried out according to parametric approaches. The interpolation technique using GIS functions helped in managing the spatial data and visualizing the results. Our results indicated that the most important limiting factors for irrigated wheat and barley cultivations are soil physical and fertility/chemical properties, while the production of irrigated grain maize and sorghum is mainly limited by climatic conditions at Mashhad Plain. It was shown that all land units suitable for irrigated wheat cultivation overlap with that of barley, whereas the same limiting factors resulted in the overlapping of the irrigated grain maize production area with that of sorghum. The results of the physical land suitability evaluation for specific cereal crops indicated the priority of irrigated barley and wheat cultivations over irrigated grain maize and sorghum at the study area.

Prediction of Shear Wave Velocity in Underground Layers Using SASW and Artificial Neural Networks

This research aims at improving the methods of prediction of shear wave velocity in underground layers. We propose and showcase our methodology using a case study on the Mashhad plain in north eastern part of Iran. Geotechnical investigations had previously reported nine measurements of the SASW (Spectral Analysis of Surface Waves) method over this field and above wells which have DHT (Down Hole Test) result. Since SASW utilizes an analytical formula (which suffers from some simplicities and noise) for evaluating shear wave velocity, we use the results of SASW in a trained artificial neural network (ANN) to estimate the un- known nonlinear relationships between SASW results and those obtained by the method of DHT (treated here as real values). Our results show that an appropriately trained neural network can reliably predict the shear wave velocity between wells accurately.