Showing 17 results for Prediction
M.j. Nazemosadat, A.r. Sepaskhah, S. Mohammady,
Volume 5, Issue 3 (10-2001)
Abstract
In the Islamic Republic of Iran, the occurrence of chilling and freezing stresses have frequently caused great damages to crops and horticultural products. In southern Fars Province (south Iran) the cultivation of citrus orchards is popular and the economic losses due to injury from chilling and freezing stresses may exceed billions of Rials annually. The drop of ambient air temperature (above zero) reduces the ordinary metabolism activity of plants and causes chilling stress. If the temperature drops below zero and remains there for a considerable time, intercellular freezing may occur. This process always kills the cells and provokes tissue injury. In the present study, the possibility of predicting daily minimum temperature using the dew point of a previous day measured at 18:30 was examined.
It was found that the prediction of minimum temperature is possible if the dew points are modified on the basis of the air relative humidity. For the episodes that relative humidity varies from 45% to 55%, minimum temperature at day i+1 was found to be almost equal to the dew point on the previous day (day i). For the periods that relative humidity is above (below) this range, the minimum temperature on day i+1 was observed to be greater (lower) than the estimated dew point on day i.
M. M. Ghasemi, A. R. Sepaskhah,
Volume 8, Issue 1 (4-2004)
Abstract
The vast pastures and agricultural development plans for dry farming and irrigated farming in Khuzestan Province depend on rain. This requires availability of annual precipitation prediction models to be used in the management decision-making process. In this research, the long-term daily precipitation data from 15 rain gauge stations in the study area were collected for study and a relationship between the early fall season precipitations of 42.5 mm (t42.5) and the annual precipitation was obtained. The results showed that the relationship was an inverse one such that the later the fall precipitation occurred, the greater the annual precipitation would be. To increase the coefficient of determination in the models, climatic variables such as Persian Gulf sea surface temperature and geographical characteristics (longitude, latitude, altitude, and long term mean annual precipitation) were used. Except for the long term mean annual precipitation and altitude, other variables did not increase the coefficient of determination. The final simple model found is as follows: Pa=184.787-1.891t42.5+0.855Pm , R2=0.704 where, Pa is the annual precipitation, t42.5 is the time from beginning of fall season for 42.5 mm of precipitation, and Pm is the long term mean annual precipitation.
S. M. J. Nazemosadat, A. Shirvani,
Volume 8, Issue 1 (4-2004)
Abstract
In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (southern oscilation index (SOI) and SST (sea surface temperature) over Nino's area (Nino's SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali.
The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficents between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino's SST (Nino4 in particular) was found to be around 10% more influential than SOI.
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S. R. Hasan Beygi Bidgoli, B. Ghobadian, P. Nassiri, N. Kamalian,
Volume 8, Issue 4 (1-2005)
Abstract
In addition to farm operations, power tillers in Iran are also engaged in load and passenger transportation. Inspite of their noise and adverse effects on power tiller drivers and bystanders, they have not been adequatly investigated. The initial survey in the present investigation on a 13-hp power tiller at 2200 rpm engine speed revealed that its noise was 92 dB(A), compared to the standard limit of 85 dB(A) which is disappointing. The test site was prepared according to international standards and the noise signals emitted from the system were measured and analyzed in time and frequency domains for audio frequency range (20 – 20000 Hz).
The results showed that the noise intensity was higher by 7.74 to 10.75 dB(A) for the microphone position at driver’s ear compared to the bystanders position and that the engine speed played a great role in noise generation for power tiller. This is because the noise increases up to 8.5 dB(A) with engine speed variations. Finally, the power tiller prediction models of sound pressure levels at driver’s ear and bystanders were determined using the experimental data.
M. T. Dastorani,
Volume 11, Issue 40 (7-2007)
Abstract
The potential of artificial neural network models for simulating the hydrologic behaviour of catchments is presented in this paper. The main purpose is the modeling of river flow in a multi-gauging station catchment and real time prediction of peak flow downstream. The study area covers the Upper Derwent River catchment located in River Trent basin. The river flow has been predicted (at Whatstandwell gauging station) using upstream measured data. Three types of ANN were used for this application: Multi-layer perceptron, Recurrent and Time lag recurrent neural networks. Data with different lengths (1 month, 6 months and 3 years) have been used, and flow with 3, 6, 9 and 12 hours lead-time has been predicted. In general, although ANN shows a good capability to model river flow and predict downstream discharge by using only upstream flow data, however, the type of ANN as well as the characteristics of the training data was found as very important factors affecting the efficiency of the results.
M Noruzi, A Jalalian, Sh Ayoubi, H Khademi,
Volume 12, Issue 46 (1-2009)
Abstract
Crop yield, soil properties and erosion are strongly affected by terrain parameters. Therefore, knowledge about the effects of terrain parameters on strategic crops such as wheat production will help us with sustainable management of landscape. This study was conducted in 900ha, of Ardal district, Charmahal and Bakhtiari Province to develop regression models on wheat yield components vs. terrain parameters. Wheat yield and its components were measured in 100 points. Points were distributed randomly in stratified geomorphic surfaces. Yield components were measured by harvesting of 1 m2 plots. Terrain parameters were calculated by a 3×3 m spacing from digital elevation model. The result of descriptive statistics showed that all variables followed a normal distribution. The highest and lowest coefficient of variance (CV) was related to grain yield (0.36) and thousand seeds weight (0.13), respectively. Multiple regression models were established between yield components and terrain parameters attributes. The predictive models were validated using validation data set (20% of all data). The regression analysis revealed that wetness index and curvature were the most important attributes which explained about 45-78% of total yield components variability within the study area. The overall results indicated that topographic attributes may control a significant variability of rain-fed wheat yield. The result of validation analysis confirmed the above-stated conclusion with low RMSE and ME measures.
Afkhami, Dastorani, Malekinejad , Mobin,
Volume 14, Issue 51 (4-2010)
Abstract
Drought is a natural feature of the climate condition, and its recurrence is inevitable. The main purpose of this research is to evaluate the effects of climatic factors on prediction of drought in different areas of Yazd based on artificial neural networks technique. In most of the meteorological stations located in Yazd area, precipitation is the only measured factor while generally in synoptic meteorological stations in addition to precipitation some other variables including maximum and mean temperature, relative humidity, wind speed, dominant wind direction and the amount of evaporation are also available. In this research it was tried to evaluate the role of the type and number of meteorological factor (as inputs of ANN model) on accuracy of ANN based drought prediction. Research area is a part of Yazd province containing only one synoptic and 13 non-synoptic meteorological stations. Three-year moving average of monthly precipitation was the main input of the models in all stations. The type of ANN used in this study was time lag recurrent network (TLRN), a dynamic architecture which was selected by evaluation of different types of ANN in this research. What was predicted is the three-year moving average of monthly precipitation of the next year, which is the main factor to evaluate drought condition one year before it occurs. For the Yazd synoptic meteorological station, several combinations of input variables was evaluated and tested to find the most relevant type of input variables for prediction of drought. However, for other 13 stations precipitation data was the only variable to use in ANN models for this purpose. Results in all stations were satisfactory, even where only one input (precipitation) was used to the models, although the level prediction accuracy was different from station to station. Result taken from this research, indicates high flexibility of ANN to cope with poor data condition where it is difficult to get acceptable results by most of the methods.
A. Vaezi, M. Abbasi,
Volume 16, Issue 61 (10-2012)
Abstract
The Soil Conservation Service Curve Number (SCS-CN) method is widely used for predicting direct runoff from rainfall events. The ratio of initial abstraction (λ=Ia/S) to maximum potential retention (S) was assumed in its original development to be equal to 0.2 (λ=Ia/S=0.2) in SCS-CN method. Application of the initial abstraction ratio equal to 0.2 out of the area where it has been developed may lead to a non logical estimation of runoff. Thus, the study was conducted to determine the initial abstraction ratio (λ=Ia/S) by analyzing measured rainfall-runoff events. The dataset consisted of 58 rainfall-runoff events during 15 years (1987-2001) of rainfall and runoff measurements from Taham-Chay watershed, northwest of Zanjan, Iran. Based on the results, the estimated runoff value on the basis of Ia= 0.2S was 26.7 times higher than the measured value, on average. There was a very low relationship between the measured and estimated runoff values (R2=0.09) and mean model error was 0.13. The Ia/S values varied from 0.004 to 0.008 with an average of 0.006. When Ia/S value was modified to 0.08, ratio of the measured to estimate runoff value was 1.4 and the determination coefficient (R2) of the relationship between the two was 0.41. When seven rainfall events that had the low rainfall intensity values (lower than 0.14 mm/h) and two events that had the high rainfall depth (bigger than 10.47 mm) during the past five days were removed from the data analysis process, ratio of the measured to estimated runoff value decreased to 1.3 and the determination coefficient (R2) of the relationship between the two enhanced to 0.90. The mean model error for the modified Ia/S value also decreased to 0.007. It also improved model efficiency coefficient (EF) to -0.089 compared with 0.91 for traditional Ia/S value (0.2).
H. Nazaripour, Z. Karimi, M. Sedaghat,
Volume 20, Issue 75 (5-2016)
Abstract
Drought is a climatic anomaly that associates with a significant decrease (lack) of precipitation and water resources availability, which spreads on vast temporal and spatial scales, and significantly affects various aspects of life and environment. One of the most common methods of drought assessing and monitoring is calculating drought indices (DIs). Drought areal and temporal extent and its severity are determined by these indices. In this study, an aggregate drought index (Hydro-Meteorological) has been developed for the assessment of hydrological and meteorological droughts in Sarbaz river basin located in southeastern of Iran. The Aggregate Drought Index (ADI) comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. In this case, monthly values of Stream flow Drought Index (SDI) and Standardized Precipitation Index (SPI) indicators were used for four similar reference periods with principle component analysis and aggregate hydro-meteorological index was defined based on its first component. The study time span was set between 1981-82 to 2010-11, which begins of October in Iran. Results based on the aggregate drought index (ADI) revealed that a long period of hydro-meteorological drought occurred from 1999-2000 to 2005/06 in southeast of Iran, in which, 2003/04 water year has been extremely a drought year. The ADI methodology provides a clear, objective approach for describing the intensity of drought. This index is appropriately able to represent the behavior of Hydro-Meteorological droughts and recommended as an integrated index for assessing and monitoring of regional droughts. Finally, different states of hydro-meteorological drought have been extracted based on conventional regional thresholds, and have been modeled by Markov chain. This made the estimation of drought state transition frequency possible, and made the prediction of next drought state time more real. State transition frequency matrices, are the main instruments for predicting drought states in real time. Results of validation tests and conforming the predicted results with real data indicate that predicting hydrological drought state transitions in the study area using Markov chain method is valid.
M. Sadeghian, H. Karami, S. F. Mousavi,
Volume 21, Issue 4 (2-2018)
Abstract
Nowadays, greater recognition of drought and introducing its monitoring systems, particularly for the short-term periods, and adding predictability to these systems, could lead to presentation of more effective strategies for the management of water resources allocation. In this research, it is tried to present appropriate models to predict drought in city of Semnan, Iran, using time series, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (MLP and RBF). For these modeling processes, average monthly meteorological parameters of rainfall, temperature, minimum temperature, maximum temperature, relative humidity, minimum relative humidity, maximum relative humidity and SPI drought index were used during the period 1966 to 2013. The results showed that among the many developed models, the ANFIS model, with input data of average rainfall, maximum temperature, SPI and its last-month value, 10 rules and Gaussian membership function, showed appropriate performance at each stage of training and testing. The values of RMSE, MAE and R at training stage were 0.777, 0.593 and 0.4, respectively, and at testing stage were 0.837, 0.644 and 0.362, respectively. Then, the input parameters of this model were predicted for the next 12 months using ARIMA model, and SPI values were predicted for the next 12 months. The ANN and time series methods with low difference in error values were ranked next, respectively. The input parameters SPI and temperature had better performance and rainfall parameter had weaker performance.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (2-2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
S. V. Razavi Termeh, K. Shirani, M. Soltani Rabii,
Volume 23, Issue 2 (9-2019)
Abstract
Today, supplying water to meet the sustainable development goals is one of the most important concerns and challenges in most countries. Therefore, identification of the areas with groundwater potential is an important tool for conservation, management and exploitation of water resources. The purpose of this research was to prepare the potential groundwater map in Nahavand, Hamedan Province, using the weight of evidence model and combining it with logistic regression. For this purpose, the information layers of slope angle, slope aspect, slope length, altitude, plan curvature, profile curvature, TWI, SPI, distance from fault, fault density, distance from river, drainage density, lithology and land use were identified as the factors affecting groundwater potential and digitized in the ArcGIS software. After designing the groundwater potential map with these three methods, ROCs were used to evaluate the results. Of 273 springs identified in this study, 191 (70%) were used to prepare the groundwater potential map and 82 springs (30%) were used to evaluate the model. The area under curve (AUC) obtained from the ROC curve showed an accuracy of 80.4% for the weight of evidence model and 82.5% for the weight of the evidence- regression combined model
M. Boustani, F. Mousavi, H. Karami, S. Farzin,
Volume 23, Issue 4 (12-2019)
Abstract
River discharge is among the influential factors on the operation of water resources systems and the design of hydraulic structures, such as dams; so the study of it is of great importance. Several effective factors on this non-linear phenomenon have caused the discharge to be assumed as being accidental. According to the basics the chaos theory, the seemingly random and chaotic systems have regular patterns that are predictable. In this research, by using methods of phase space mapping, correlation dimension, largest Lyapunov exponent and Fourier spectrum power, a period covering 43 years of Zayandehrud River discharge (1971-2013) was evaluated and analyzed based on the chaos theory. According to the results, the non-integer value of the correlation dimension for Eskandari and Ghale Shahrokh stations (3.34 and 3.6) showed that there was a chaotic behavior in the upstream of Zayandehrud-Dam Reservoir. On the other hand, in the Tanzimi-Dam station, the correlation dimension curve was ascending with respect to the embedding dimension, showing that the studied time-series in the downstream of Zayandehrud-Dam Reservoir was random. The slope of the Lyapunov exponent curve for Eskandari, Ghale Shahrokh and Tanzimi-Dam stations was 0.0104, 0.017 and 0.0192, respectively, and the prediction horizon in the chaotic stations was 96 and 59 days. The non-periodical feature of time series was studied by using the Fourier spectrum power. The wide bandwidth, besides other indices, showed that river discharge in the upstream stations of Zayandehrud Reservoir was chaotic.
L. Cheraghpoor, M. Pajoohesh, A. Davoodyan, A. Bozorgmehr,
Volume 23, Issue 4 (12-2019)
Abstract
River discharge is among the influential factors on the operation of water resources systems and the design of hydraulic structures, such as dams; so the study of it is of great importance. Several effective factors on this non-linear phenomenon have caused the discharge to be assumed as being accidental. According to the basics the chaos theory, the seemingly random and chaotic systems have regular patterns that are predictable. In this research, by using methods of phase space mapping, correlation dimension, largest Liapunov exponent and Fourier spectrum power, a period covering 43 years of Zayandehrud River discharge (1971-2013) was evaluated and analyzed based on the chaos theory. According to the results, the non-integer value of the correlation dimension for Eskandari and Ghale Shahrokh stations (3.34 and 3.6) showed that there was a chaotic behavior in the upstream of Zayandehrud-Dam Reservoir. On the other hand, in the Tanzimi-Dam station, the correlation dimension curve was ascending with respect to the embedding dimension, showing that the studied time-series in the downstream of Zayandehrud-Dam Reservoir was random. The slope of the Lyapunov exponent curve for Eskandari, Ghale Shahrokh and Tanzimi-Dam stations was 0.0104, 0.017 and 0.0192, respectively, and the prediction horizon in the chaotic stations was 96 and 59 days. The non-periodical feature of time series was studied by using the Fourier spectrum power. The wide bandwidth, besides other indices, showed that river discharge in the upstream stations of Zayandehrud Reservoir was chaotic.
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (7-2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.
H. R. Matinfar, Z. Mghsodi, S. R. Mossavi, M. Jalali,
Volume 24, Issue 4 (2-2021)
Abstract
Knowledge about the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. During the last two decades, the utilization of data mining approaches in spatial modeling of SOC using machine learning algorithms have been widely taken into consideration. The essential step in applying these methods is to determine the environmental predictors of SOC optimally. This research was carried out for modeling and digital mapping of surface SOC aided by soil properties ie., silt, clay, sand, calcium carbonate equivalent percentage, mean weight diameter (MWD) of aggregate, and pH by machine learning methods. In order to evaluate the accuracy of random forest (RF), cubist, partial least squares regression, multivariate linear regression, and ordinary kriging models for predicting surface SOC in 141 selected samples from 0-30 cm in 680 hectares of agricultural land in Khorramabad plain. The sensitivity analysis showed that silt (%), calcium carbonate equivalent, and MWD are the most important driving factors on spatial variability of SOC, respectively. Also, the comparison of different SOC prediction models, demonstrated that the RF model with a coefficient of determination (R2) and root mean square error (RMSE) of 0.75 and 0.25%, respectively, had the best performance rather than other models in the study area. Generally, nonlinear models rather than linear ones showed higher accuracy in modeling the spatial variability of SOC.
A. Ghorbani, M. Moameri, F. Dadjou, L. Andalibi,
Volume 25, Issue 2 (9-2021)
Abstract
The purpose of this study was to model biomass with soil parameters in Hir-Neur rangelands of Ardabil Province. Initially, considering the vegetation types and different classes of environmental factors, at the maximum vegetative growth stage, using one square meter plot, biomass was estimated by clipping and weighing method. For each transect, a soil sample was taken and transferred to the soil laboratory and the various parameters were measured by conventional methods. The relationship between soil factors and the rangeland biomass was analyzed and simulated using linear multiple regression. Among the measured soil factors, the Silt, EC, Ca, Ksoluble, OC, POC, pH, Mg, TNV, clay, P, and volumetric moisture had the highest effect and percentage of biomass forecast (p<0.01). The accuracy of the simulated maps was analyzed using RMSE criteria and for grasses, forbs, shrubs, and total biomass were equal to 0.81, 0.65, 0.34, and 0.46, respectively. The results of this study, not only point out the importance of soil factors on the biomass but also as a baseline data for managing rangelands, supply-demand, and carbon balance can be used in the current section.
