جستجوی مقالات مرتبط با کلیدواژه « logistic regression » در نشریات گروه « آب و خاک »

Considering the importance of fresh water for human life and the vulnerability of groundwater sources to all kinds of pollution and the possibility of transferring pollutants to other surface and groundwater sources, as well as the location of Iran in the arid and semi-arid belt, protecting this valuable and rare element is imperative and its continuous monitoring must be one of the priorities of water resources managers. Therefore, in the current research, nitrate pollution in the eastern plains of Mazandaran province was discussed and relevant issues were investigated, and an efficient and optimal model for predicting nitrate concentration was presented. In this research, three machine learning models including decision tree, logistic regression and artificial neural network were compared. The physical and chemical data measured during the years 1985 to 2020 were used and entered as the input variables of the models. The variables include temperature, water level, pH, EC, HCO3-, CL-, SO24-, Na+, K+, Mg2+, Ca2+, TH and TDS; The amount of nitrate contamination of the groundwater, was predicted by dividing 70% of the dataset as training and 30% as testing data. The R2, RMSE, NSE and PBIAS indexes were applied for model evaluation. The results indicated that the Decision Tree model had the best performance with a large difference compared to the other two models (R2 = 0.957 and RMSE = 0.297, NSE = 0.95 and testing acc = 0.907). After That, logistic regression and artificial neural network had much weaker performances than the lead model. It is suggested to conduct another research with other machine learning models by changing the input variables and add some extra ones such as land-use and compare the results with the current research.

Predicting the potential of groundwater is very important for the systematic development and planning of water resources. The main purpose of this study was to develop ensemble machine learning models including random forest (RF), logistic regression (LR) and Naïve Bayes (NB) by random subspace Classifier (RS) algorithm to predict groundwater potential areas in Birjand plain. Therefore, for implementation, geo-hydrological data of 37 groundwater wells (Number of wells, location of wells and groundwater level or Water table) and 17 hydrology, topographic, geological and environmental criteria were used. The least squares support vector machine (LSSVM) feature selection method used to determine the effective criteria to increase the performance of machine learning algorithms. Finally, groundwater potential prediction maps were prepared using RF-RS, LR-RS and NB-RS models. The performance of these models evaluated using the area under the curve (AUC) and other statistical indicators. The results showed that the RF-RS hybrid model (AUC = 0.867) has a very high predictability for groundwater potential in the study area. It was also found that the elevation criterion is most important in predicting the groundwater potential in the study area. The results of the present study can be useful for making appropriate decisions and planning regarding the optimal use of groundwater resources.

Precipitation forecasting is an essential tool for optimal management of water resources and flood forecasting. The numerical weather prediction (NWP) models play a major role in weather forecasting. They predict the future state of the weather by mathematical modeling of the atmosphere behavior, based on its current condition. However, the accuracy of the NWP models is still a challenging issue and its improvement is the main goal of the operational prediction centers. In weather forecasting by using NWP models, there are several resources of uncertainty such as intrinsic chaotic behavior of atmosphere dynamic system, errors in observational data and initial conditions which are almost impossible to remove. Ensemble systems are used to quantify these uncertainties. Instead of only one deterministic forecast, an ensemble system is created by several forecasts obtained from perturbation of the initial conditions, physical schemes or dynamical core of the NWP models. Ensemble systems are widely used by the meteorological communities especially for medium-range weather forecasts, short range and even ultra-short range weather prediction. A probabilistic forecasting of flood and extreme precipitation can be produced by an Ensemble Prediction System (EPS). However in practice, ensemble forecasts are generally under-dispersive and thus are not calibrated, especially for meteorological parameters near the ground level. Several statistical methods have been proposed to post-process the EPS outputs. After post-processing the EPS outputs, the biases in both location and dispersion are removed using a historical database of ensemble forecast errors, and then a predictive probability density function (PDF) can be estimated. The most popular ensemble post-processing methods are Bayesian Model Averaging and Ensemble Model Output Statistics. In BMA method, based on the error statistics of each member during a training period, a PDF is first fitted to every ensemble member forecast. Then the predictive PDF is estimated by weighted averaging of members' PDFs. Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity Logistic regression is a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predictand is a probability rather than a mea- surable physical quantity LR was among the first statistical methods that were used to post-process the EPS output. Logistic Regression (LR) was extended to provide a full continuous predictive PDF. In the extended Logistic regression (ELR), the predictions and thresholds are used as additional predictor variables. In this study, an EPS was developed using eight different configurations of the WRF model to produce probabilistic precipitation forecast over Iran. Initial and boundary conditions for WRF were provided from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecasts with a horizontal resolution of 0.5. The BMA and ELR methods were used to calibrate the probabilistic forecasts of rainfall in the fall and winter of 2016-2015 over Iran. The data were separated to two parts of equal periods. The first and second parts of the data were used for training and test respectively. The calibrated probabilistic forecasts were assessed using reliability and Relative Operating Characteristic Curve (ROC) diagrams, ROC Skill Score, Ranked Probability Score (RPS) and Ranked Probability Skill Score RPSS at the thresholds of 0.1, 2.5, 5, 10, 15 and 25 mm. For ensemble probabilistic forecasting, BMA was used as a statistical technique that combines inferences and predictions based on individual ensemble members, so as to yield a more skillful and reliable probabilistic prediction. It was assumed that the forecast PDF of a weather variable y is conditional on the ensemble member forecast f_k: p_k=(y|f_k). The BMA ensemble forecast is essentially an average of forecasts based on individual members weighted by the likelihood that an individual forecasting model is correct given the observations. LR was used as a nonlinear regression method that is well suited to probability forecasting, i.e. situations where the predicted is a probability rather than a measurable physical quantity. The mathematical form of the LR equation yields ‘S-shaped’ prediction functions that are strictly bounded on the unit interval (0<p< 1).

Parts of the Plain are different roles as pollutants velocity and reach into groundwater. Land assessment and their proper management for a variety of land uses, due to their susceptibility to transfer contaminations is essential. DRASTIC method as overlaying way has the seven influencing parameters for contamination susceptibility mapping. Due to local effects on DRASTIC model parameters, coefficients modify for the input data is required. . According to multiple studies in the Hashtgerd plain, to assess the aquifer vulnerability, modified DRASTIC method, logistic regression-DRASTIC and hierarchical analysis process DRASTIC was used. In addition to the DRASTIC input parameters, land uses were used in analysis considering its role in the production of contamination. The western part of the study area, there is an aquifer that charged from the eastern of the plain. DRASTIC model as output of the model validate with nitrates and eastern areas was excluded. In validating indicators of vulnerability, Spearman correlation, are calculated respectively 0.79, 0.84, 0.86 and 0.91 for DRASTIC, modified DRASTIC, logistic regression-DRASTIC and hierarchical analysis process –DRASTIC and Analytical Hierarchy Process has the highest correlation coefficient.

The aim of this study is to prepare the groundwater spring potential map using Weight of Evidence, logistic regression, and frequency ratio methods and comparing their efficiency in Chehlgazi watershed, province of Kurdistan. At first, 17 effective factors in springs occurrence including geology, distance to fault, fault density, elevation, relative permeability of lithological units, slope steepness, slope aspect, plan curvature, profile curvature, precipitation, distance to Stream, drainage Stream density, Sediment Transport Capacity Index (STCI), Stream Power Index, topographic wetness index (TWI) and land use/land cover (LU/LC) were selected. The validation processes of methods were conducted by relative performance characteristic curve (ROC). The area under an ROC curve (AUC) for the weight of evidence, logistic regression and frequency ratio was 85/8%, 79% and 89%, respectively. The results showed that all methods are suitable estimator for mapping the groundwater spring potential in the study area. But the frequency ratio method with the most amounts is the best method to produce and map the groundwater spring potential. Also, validation of the mappings based on the percentage of pilot springs, training springs and all springs showed that the logistic regression, WoE and frequency ratio, with 45, 56 and 45 percent of spring occurrence on the high potential classes respectively, had the highest validation.