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Showing 32 results for Indices
Effect of Bottom Slope on Determining Optimum Coefficients and Performance of PID Controller in Irrigation Canals
Sh. Zamani, A. Parvaresh Rizi, S. Isapour,
Volume 17, Issue 66 (2-2014)
Abstract
Modernization of irrigation canals as an operation improvement tool is essential to promote the performance of canal networks and indeed requires control systems. Proportional integral derivative (PID) algorithms have more applications than the other controllers in different places of the world, but tuning these controllers for different hydraulic conditions of canals is considered as a major problem for designing control algorithms. Since the bottom slope is one of the effective factors in the water flow dynamic behavior, in this research, the distant downstream Proportional Integral Derivative feedback control with decouplers was designed with a change in longitudinal slope in a reference canal and its performance was investigated. The canal characteristics were used to tune this controller and the system identification as a new method was applied for determining canal characteristics. SOBEK hydrodynamic model modulated with MATLAB software was used to design and run the control algorithms, and slope influence on water flow behavior, tuning controller, and coefficients of controller were investigated with different values of slope. Then, controller performance for hypothetical period of operation in various scenarios was evaluated with computation performance indices. The results showed less resonance behavior of water flow and less potential of controller in steep slope
Assessing Current State of Desertification Based on Water, Climate and Soil Indicators Using IMDPA Model (Case Study: Dashte Abbas)
M. Mombeni, A. Karamshahi, Graee, F. Azadnia, H. Khosravi,
Volume 19, Issue 72 (8-2015)
Abstract

Desertification is currently a big problem in many countries, especially in developing countries, and includes natural processes and improper human activities. The present study was conducted to evaluate the potential of desertification with an emphasis on water, climate and soil criteria using IMDPA Model in Abbas plain, Ilam province with an area about 18028.8 hectare. Geometric averages for indices including water table fluctuations, EC of water, SAR, irrigation system, annual precipitation, aridity index, drought continuity index, soil texture, soil thickness and Ec of soil were obtained using ArcGIS 9.3 and the status map of each criterion was prepared. The results of climate indicated that 100% of the area is in severe class. The desertification intensity map based on soil criteria demonstrated that over 4843 hectare (28.86 percent of total area) and 13185 hectare (73.13 percent of total area) are in low and moderate classes, respectively. Also, the obtained results from geometric average of water criteria indices showed that 10861.4 hectare (60.2 percent of total area) and 7166.6 hectare (39.75 percent of total area) are in low and moderate classes, respectively. The results also indicated that climate with the value of 2.81 is the most influential criterion in the severity of desertification in the study area. Accordingly, it can be said that the quantitative value of desertification intensity of total area is in moderate class.


Comparison of Rangeland Vegetation Cover and Soil Properties Variations Affected by Flood Spreading, Enclosure and Grazing Uses
F. Jalilian, B. Behmanesh, M. Mohammad Esmaeili, P. Gholami,
Volume 21, Issue 2 (8-2017)
Abstract

In this study, different indices of vegetation cover variations and different physicochemical properties of soil in three treatments of flood spreading, enclosure and grazing (control) were investigated and compared in in the region of Peshert in Mazandaran province. In order to measure different soil characteristics, 18 soil samples (six withdrawals at any treatment) from a depth of zero to 30 cm were taken from the desired treatments. In order to investigate different vegetation indices, a total of 90 plots (nine transects of 100 m) were run using systematic random sampling in the studied treatments and the necessary measurements were done (30 plots at any treatment). Then, in each of these plots, canopy coverage percentage was determined separately for each species and to evaluate and assess the diversity and richness in all three treatments, Shannon-Wiener and Simpson diversity indices and Menhink and Margalef richness indices were used. Finally, the data obtained from both sections of soil and vegetation in three studied treatments were compared and analyzed using one-way ANOVA and Duncan test. The results showed that floodwater spreading and enclosure significantly increased the percentage of sand and total Nitrogen, and significantly reduced the percentage of silt and potassium compared to control treatment. Also, percentage of clay and organic matter, soil pH levels, conductivity and soil phosphorus showed no significant differences in the treatments under study. The results of variance analysis of various indices of diversity, richness and species evenness showed that all indicators had significant responses in three treatments and the highest diversity and species richness were observed in flood spreading and enclosure treatments. Due to changes in soil properties and vegetation in flood spreading and enclosure treatments compared to the control treatment, it can be stated that operations of floodwater spreading and enclosure in the studied region has had positive effect on modification of soil texture, increasing the permeability of the soil and ultimately improvement of the vegetation.


Desertification Risk Mapping based on Water Resources Degradation using Multi Criteria Decision Making (Case Study: Kashan Plain)
S. Pishyar, H. Khosravi, A. Tavili, A. Malekian,
Volume 21, Issue 4 (2-2018)
Abstract
In this study, to study the status of water resources degradation in Kashan region, Isfahan Province, eight indices including: drop in groundwater, water salinity, irrigation efficiency, Well-to-Qanat development ratio, the pumping time, shortage of water supplies for animals and humans and the water negative balance were selected according to previous studies conducted on desertification in Iran and the world. Existing evaluation models were determined. Desertification map of the study area was provided according to MEDALUS model and selected indices. The selected indices were weighted using a multi-criteria decision method and each index having weight more than 0.5 were selected as the most effective indices of desertification. Again, the desertification status map of the study area was prepared by the most effective indices. Finally, the two desertification maps were compared. The results showed that the drop in groundwater, water salinity, the pumping time and water negative balance have the most effect on water resources degradation among selected indices. The results of comparing two groundwater degradation maps showed that based on map provided with eight indices, 87.78 and 8.30 percent of the total area are classified in critical conditions c and b, respectively. While the map provided by the most effective indicators shows that 99.15% of the total area is classified in the critical condition "c" and just 0.849% is classified in the critical condition "b".  It can be concluded that to assess desertification status, it is better to first determine the indicators by weighting and prioritizing methods. This will identify the indicators that have not had a significant effect on the desertification phenomenon in the area and prevent their impact on desertification classes and reduction of scores.

Investigation of Pedodiversity Indices, Soil Differentiation and Soil-Landscape Evolution in the Semi-Arid Region of Chaharmahal-Va-Bakhtiyari Province
M. Zeraatpisheh, Sh. Ayoubi, H. Khademi, A. Jafari,
Volume 23, Issue 1 (6-2019)
Abstract
Landscapes are considered as a series of different land units with a size, shape and location arrangement that are permanently under the influence of natural events and human activities. Understanding the dynamics and heterogeneity of landscapes and environmental changes is of great importance. In order to quantitatively analyze and interpret the factors affecting the changes in the environment and terrain diversity, diversity indices were used to analyze the ecosystem. In this study, the relationships between soils evolution and geomorphic surfaces were investigated by applying pedodiversity indices in a part of a semi-arid region of Chaharmahal-Va-Bakhtiyari Province. In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. The results showed that soil evolution in the studied area was mostly influenced by topography, parent material and the underground water level; that is, in the higher lands, the lowest evolution was observed while in the plain ones, the soil of the higher evolution observed. In addition, the effect of geomorphic surfaces were obvious. Pedodiversity indices increased under the decrease of the hierarchy levels. In addition, the obtained equations revealed the nonlinear relationships in the area of geomorphic surfaces. The positive and nonlinear relationship between pedodiversity indices confirmed the nonlinear dynamic system in the studied soils.

Estimation of Reference Evapotranspiration Using Artificial Neural Network Models and the Hybrid Wavelet Neural Network
N. Ganji Khorramdel, S. M. R. Hoseini,
Volume 23, Issue 2 (9-2019)
Abstract
Estimation of evapotranspiration is essential for planning, designing and managing irrigation and drainage schemes, as well as water resources management. In this research, artificial neural networks, neural network wavelet model, multivariate regression and Hargreaves' empirical method were used to estimate reference evapotranspiration in order to determine the best model in terms of efficiency with respect to the existing data. The daily data of two meteorological stations of Shahrekord and Farrokhshahr airport in the dry and cold zones of Shahrekord during the period 2013-2004 was used; these included the minimum and maximum temperature, the average nominal humidity, wind speed at 2 meters height and sunshine hours. %75 of the data were validated, and %25 of the data was used for testing the models. Designed network is a predictive neural network with an active sigmoid tangent function hidden in the layer. In the next step, different wavelets including Haar, db and Sym were applied on the data and the neural network-wavelet was designed. To evaluate the models, the method was used by the Penman-Montith Fao and for all four methods, RMSE, MAE and R statistical indices were calculated and ranked. The results showed that the wave-let- neural network with the db5 wavelet had a better performance than other wavelets, as well as the artificial neural network, multivariate regression and the Hargreaves method. The results of wavelet network modelling with the db5 wavelet in the Farrokhshahr station were calculated to be 0.2668, 0.2067 and 0.998, respectively; at the airport station, these were equal to 0.2138, 0.14 and 0.9989, respectively. The results, therefore, showed that the neural network-wavelet performance was more accurate than the other models studied in this study.

Regionalization Analysis of SPI and SPEI Drought Indices for Karoon Basin
M. Saeidipour, F. Radmanesh, S. Eslamian, M. R. Sharifi,
Volume 23, Issue 2 (9-2019)
Abstract
The current study was conducted to compute SPI and SPET drought indices due to their multi-scale concept and their ability to analyze different time-scales for selected meteorological stations in Karoon Basin. Regionalization of SPI and SPEI Drought indices based on clustering analysis was another aim of this study for hydrological homogenizing. Accordingly, to run test through data and determine similar statistical periods, 18 stations were selected. SPI and SPEI values were plotted in the sequence periods graphs and their relationships were analyzed using the correlation coefficient. The results were compared by Pearson correlation coefficient at the significance level of 0.01. The results showed that correlation coefficients (0.5-0.95) were positive and meaningful for all stations and the correlation coefficient between the two indices were increased by enhancing the time-scales. Also, time-scales enhancement decreased the frequency of dry and wet periods and increased their duration. Through regionalization of basin stations based on clustering analysis, the stations were classified into 7 classes. The results of SPEI regionalization showed that the frequency percentage of the normal class was more than those of dry and wet classes.

Analyzing Piezometers’ Behavior to Determine the Lag Time of the Rainfall Effects on the Groundwater Level Fluctuations in the Alluvial Plain of Shiraz by Using SPI and GRI Indices
M. Noshadi, A. Ahadi,
Volume 23, Issue 4 (12-2019)
Abstract
Groundwater supplies a major portion of two basic human needs: drinking and agricultural water. Forecasting, monitoring, evaluating the performance and planning of this vital resource require modelling. The lag time of the groundwater level fluctuations against the rainfall is one of the essential data of the models. The purpose of the present study was to evaluate the piezometers behaviour by using the Pearson cross-correlation method between SPI and GRI indices in the Shiraz alluvial plain in order to determine the mentioned lag time. The results showed a similar behaviour for 86.2% of the piezometers. In 79.3% of the piezometers, groundwater level was declined one month after the rainfall event. The best correlation coefficient between the aforementioned indices was observed along the southwestern to the northeastern axis of the plain. The northern alluvial plain has a better correlation, as compared to the southern section because of the northern-southern slope of the plain. The central area of the plain had the highest correlation coefficient. The maximum correlation coefficients occurred at a time scale of 48 months. Also, since 2004, due to the decline in the atmospheric precipitation in the Shiraz plain, the SPI index has surpassed the drought level, although the trend has not been significant. However, the GRI does not follow this trend, showing a significant hydrological drought. The reason can be the disproportionate water extraction to recharge ratio in the alluvial aquifer of the plain.

Heavy Metals Pollution Assessment in Groundwater Resources of Hamadan-Bahar Plain in 2018
A. Ghobadi, M. Cheraghi, S. Sobhan Ardakani, B. Lorestani, H. Merrikhpour,
Volume 26, Issue 1 (5-2022)
Abstract
The qualitative assessment of groundwater resources as the most important sources of drinking and agricultural water is very important. Therefore, the present study was conducted to evaluate the quality of heavy metals in groundwater resources of the Hamadan-Bahar plain in 2018 using water quality indices. In so doing, a total of 120 groundwater samples were collected from 20 stations during the spring and summer seasons and the values of physico-chemical parameters were determined based on the standard methods and also the content of heavy metals was determined using inductively coupled plasma spectroscopy (ICP). The results showed that the mean concentrations of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn (µg /L) in the samples in the spring season were 5.08, 0.260, 1.05, 2.70, 1.50, 0.490, 1.50, 7.48, and 1.75, respectively, and in the summer season were 20.7, 0.220, 0.950, 7.12, 1.34, 0.490, 1.29, 8.23, and 2.08, respectively and except for As in the summer season, the mean content of other elements was lower than maximum permissible limits established by WHO for drinking water. Meanwhile, the mean values of Cd, HPI, HEI, MI, and PoS indices in the spring season with -7.51, 9.91, 1.42, 1.42, and 328, respectively, indicate the water quality was categorized as low, low, low, low and moderately affected and in the summer season with -5.90, 10.0, 3.04, 3.04, and 673, respectively, were categorized as low, low, low, moderately affected, and high pollution. Due to the extensive use of agricultural inputs, especially chemical and organic fertilizers and chemical pesticides containing heavy metals by farmers in the study area, the possibility of increasing the concentration of heavy metals in the soil and their penetration into groundwater aquifers will not be unexpected in the medium term. Therefore, periodic monitoring in groundwater resources of the study area is recommended.

Evaluation of TRMM and ECMWF Data for Drought Time Monitoring (Case Study: Kurdistan Province)
M. Ahmadi, H. Ramezani Etedali, A. Kaviai, A.r. Tavakkoli,
Volume 27, Issue 1 (5-2023)
Abstract
Studying the effects of drought in mountainous areas is facing problems due to the inappropriate distribution of stations, the lack of long-term data, and areas lacking statistics. Therefore, the main objective of this research was to investigate the drought indices of Kurdistan province using TRMM satellite data and ECMWF dataset, as well as to evaluate their accuracy against the data of land stations in Kurdistan province. First, ECMWF precipitation data for the 2000-2020 period and TRMM precipitation data for the 2000-2019 period were obtained and evaluated using RMSE, MBE, and correlation coefficient statistics. Spearman's correlation coefficient showed a significant relationship between the TRMM satellite precipitation data and the ECMWF dataset with ground stations at the 5% level, and the value of this coefficient was between 0.95-0.85. According to the results, it can be acknowledged that the TRMM satellite rainfall and ECMWF dataset in the monthly time scale had proper accuracy at the Kurdistan province level. Therefore, these two sources were used to examine the drought indices. SPI, SPEI, and ZSI drought indices were calculated in different monthly periods (1-48), PNI in different monthly, seasonal, and annual periods in Kurdistan province (Saqqez, Qorveh, Bijar, Sanandaj stations). Spearman's correlation coefficient indicated a significant relationship at the 5% level between the SPI, ZSI, PNI, and SPEI index of the ECMWF dataset with ground stations. The results of the SPI index showed that the lowest RMSE value for the TRMM satellite at the Saqqez station and the three months was equal to 0.45, and for the ECMWF dataset at the Sanandaj station and the 24 months was equal to 0.35.

Investigation of Changes and Feasibility of Indirect Estimation of Soil Organic Carbon in Rangelands after Wildfire (Case Study: Gonbad Watershed, Hamadan)
B. Attaeian, F. Teymorie Niakan, B. Fattahi, V. Zandieh,
Volume 28, Issue 3 (10-2024)
Abstract
The objective of this study was to investigate the effect of wildfire in the rangelands of the Gonbad region of Hamedan on soil organic carbon storage in two control and fire areas after three years of fire, and the feasibility of using remote sensing in indirect estimation of soil carbon. Therefore, 20 soil surface (0-10cm depth) samples were collected from the burned area and 20 samples from the control area (40 samples in total) by the systematically random method after three years of fire time. Changes in organic carbon, total nitrogen, acidity, and salinity of surface soil were tested by independent t-test between control and fire areas. Then, to investigate the linear relationship between the storage of soil organic carbon with other parameters, the Pearson correlation was used in SPSS v. 26. The results of the independent t-test showed that there was no significant difference in EC, acidity, and soil organic carbon of the control and fire areas, but the amount of total soil nitrogen showed significantly different. The results showed a significant positive correlation was observed between soil organic carbon and total nitrogen at the level of one-hundredth of 0.830 (p< 0.01) in the fire area, and the BI index showed a significant negative correlation of 0.727 (p< 0.05). In the control area, a significant positive relationship was observed between organic carbon and total nitrogen at the rate of 0.627 (p <0.05). The results of processing Landsat 8 images (OLI-TIRS sensor) in the fire area showed that there was a statistically significant relationship between soil organic carbon and light and wetness index obtained from tasseled cap (-0.726 and 0.674, respectively) and PC1 component obtained from principal component analysis and -0.724 (p <.05). These results indicate that it is possible to use tasseled cap images to predict soil organic carbon in fire areas.

Wheat Production Simulation Using Sentinel 2 Images and Machine Learning Techniques
H. Ramezani Etedali, M. Ahmadi,
Volume 29, Issue 2 (7-2025)
Abstract
change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.

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