Showing 7 results for Regression Model
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.
M Motamednia , S.h.r Sadeghi, H Moradi, H Asadi ,
Volume 14, Issue 52 (7-2010)
Abstract
An extensive data collection on precipitation and runoff is required for development and implementation of soil and water projects. The unit hydrograph (UH) is an appropriate base for deriving flood hydrographs and therefore provides comprehensive information for planners and managers. However, UH derivation is not easy job for whole watersheds. The development of UH by using easily accessible rainfall data is then necessary. Besides that, the validity evaluation of different statistical modeling methods in hydrology and UH development has been rarely taken into account. Towards the attempt, the present study was planned to compare the efficiency of different modeling procedures in hydrograph and 2-h representative UH relationship in Kasilian watershed with concentration time of some 10h. The study took place by using 23 storm events occurred during four seasons within 33 years and applying two and multivariable regression models and 36 variables. According to the results, the median of estimated errors in estimation of 2-h UH dependent variables for verification stage varied from 37 to 88%. The results verified the better performance of cubic and linear bivariate models and logarithm-transformed data in multivariable model as well. The efficiency of multivariable models decreased when they were subjected to principle component analysis. The performance of backward method was frequently proved for estimation of dependent variables based on evaluation criteria, whereas the forward was found to be more efficient for time-dependent factors estimation.
R. Rezae Arshad, Gh. Sayyad, *, M. Mazloom, M. Shorafa, A. Jafarnejady,
Volume 16, Issue 60 (7-2012)
Abstract
Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are two methods which are used to develop PTFs. In this study, the multi-layer perceptron (MLP) neural network and backward and stepwise regression models were used to estimate saturated hydraulic conductivity using some soil characteristics including the percentage of particle size distribution, porosity, and bulk density. Data of 125 soil profiles were collected from the reports of basic soil science and land reclamation studies conducted by Khuzestan Water and Power Organization. The results showed that MLP neural network having Bayesian training algorithm with the greater coefficient of determination (R2=0.65) and the lower error (RMSE =0.04) had better performance than multiple linear regression model in predicting saturated hydraulic conductivity.
B. Farid Giglou, R. Ghazavi,
Volume 22, Issue 3 (11-2018)
Abstract
In this research, a regression model was introduced to study the mechanisms of the formation of gullies in the Quri Chay watershed, northern Ardebil province (Moghan Plain); this was done through investigating the effective factors of geo-environment and soil characteristics on the gully erosion. For this purpose, 17 gullies were randomly assigned through field surveys. Mapping and recording the morphometric of the selected gullies were performed by GPS positioning after seven rainfall events. The catchment-upper area of each gully was determined and its related physical parameters were calculated in order to investigate the effect of the physical characteristics of the catchment. Soil sampling was also done at the head of each gully at two different depths (30-30 and 60-30 cm) in order to determine the physical and chemical characteristics of the soil. According to measurement of the morphometric characteristics of the gully and soil characteristics through multivariate analysis of the data, a suitable regression model was developed for the longitudinal development of erosion after determining and calculating environmental factors related to the upper catchment of the gullies. The results of the correlation matrix between the longitudinal extension of the gully and the factors investigated indicated that the factors related to the physical characteristics of the beside watershed (area, perimeter, main stream length and average width of the catchment, main stream slope), gully morphometric characteristics (mean of gully cross section, the gully expansion area, and the gully average width) and soil characteristics (geometric mean of the aggregates diameter, lime, organic matter percentage) affected the formation and expansion of gully erosion in the Quri chay catchment. The results of regression analysis showed that the longitudinal expansion of the gully was mostly influenced by the area around each gully and the percentage of organic matter, which resulted in pressure on the rangeland and the loss of vegetation, which increased runoff and accelerated the lengthwise expansion of the gully. Also, the increase in the area of the beside catchment the gullies is known as one of the factors influencing the length of the gully, due to the high volume of runoff entering the head cut section; so it is necessary to manage runoff in the gully with the large beside catchment.
H. Asakereh, A. Shahbaee Kotenaee, M. Foroumadi,
Volume 23, Issue 1 (6-2019)
Abstract
In the vast majority parts of the Earth, a prospect now visible is the mostly synthetic thinking and fabrication by the human hand. Collision and impact of humans on the natural environment in the short and long-term courses for obvious geographical features have changed a variety of spaces. One of the consequences of human impact on the natural environment during the current period is the phenomenon of climate change. One of the climatic parameters that plays an important role in agriculture, energy, urban, tourism and road transport is the minimum temperature. In this study, an attempt was made using the minimum temperature data from 5 meteorological stations in the West Mazandaran province, as well as HADCM3 model data, to show how to change this parameter in the future periods based on simulation by the SDSM model. Accordingly, after selection of the suitable climate variables and model calibration, the accuracy of the created model in the base period was evaluated; after ensuring the sufficient accuracy of the model according to A2 and B2 scenario, data minimum temperature in 2100 was simulated. Based on the simulation results showed that the values of minimum temperature in the region over the coming years would increase. This parameter was such that the average seasonal periods 2016 to 2039, 2040 to 2069 and 2070 to 2099, as compared to the baseline period would increase, on average, by 1.8, 3.5 and 6 percent. The largest increases in the minimum temperature in the western and southern parts of the region could occur. It was also found that unlike other months of the year, the minimum temperature in January would be a decreasing trend.
S. Motalebani, M. Zibaei, A. Sheikhzeinoddin,
Volume 24, Issue 3 (11-2020)
Abstract
The interaction of population growth, technological improvement and climate change have impacted severely on agricultural and environmental sustainability. In Iran, conventional tillage practice has resulted in soil erosion and loss of soil organic matter. In this regard, Conservation Agriculture (CA) forms part of this alternative paradigm to agricultural production systems approaches and can be regarded as a means to enhancing food productivity, reducing poverty, and mitigating the consequences of climate change in rural households. The objectives of this study were to examine the determinants and impacts of CA technology on wheat yield, poverty gap and water use. To this end, an endogenous switching regression (ESR) model was employed to estimate the impacts of CA technology on continuous variables such as wheat yield, poverty gap and water use. A sample of 260 farmers from Zarghan district was selected for interview collection of necessary farm level data. The results indicated that in the select equation of ESR model, ten coefficients (out of 12) are significant at the 5% level or higher. Knowledge of soil quality, access to credit, access to information, education, farm size, ownership of machinery, participation in agricultural extension activities and farmer’ perception have positive and significant effects on the probability of adopting CA. In contrast, variables such as the distance to shopping center and number of land parcels have negative and significant influence on adoption. Also, the results of ESR model and counterfactual analysis showed that wheat yield would increase by 1.05 tons and poverty gap and water use would decrease by 20% and 910 cubic meters per hectare respectively if farmers adopt CA technology.
B. Shahinejad, A. Parsaei, A. Haghizadeh, A. Arshia, Z. Shamsi,
Volume 26, Issue 3 (12-2022)
Abstract
In this research, soft computational models including multiple adaptive spline regression model (MARS) and data group classification model (GMDH) were used to estimate the geometric dimensions of stable alluvial channels including channel surface width (w), flow depth (h), and longitudinal slope (S) and the results of the developed models were compared with the multilayer neural network (MLP) model. To develop the models, the flow rate parameters (Q), the average particle size in the floor and body (d50) as well as the shear stress (t) as input and the parameters of water surface width (w), flow depth (h), and longitudinal slope (S) were used as output parameters. Soft computing models were developed in two scenarios based on raw parameters and dimensionless form independent and dependent parameters. The results showed that the statistical characteristics in estimating w, the best performance is related to the MARS model, whose statistical indicators of accuracy in the training stage are R2 = 0.902, RMSE=1.666 and in the test phase is R2 = 0.844, RMSE=2.317. In estimating the channel depth, the performance of both GMDH and MARS models is approximately equal, both of which were developed based on the dimensionless form of flow rate as the input variable. The statistical indicators of both models in the training stage are R2 » 0.90, RMSE » 8.15 and in the test phase is R2 » 0.90, RMSE = 7.40. The best performance of the developed models in estimating the longitudinal slope of the channel was related to both MARS and GMDH models, although, in part, the accuracy of the GMDH model with statistical indicators R2 = 0.942, RMSE = 0.0011 in the training phase and R2 = 0.925, RMSE = 0.0014 in the experimental stage is more than the MARS model.
