نتایج جستجو برای: hydrology neural network and crop
تعداد نتایج: 16957948 فیلتر نتایج به سال:
Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...
Today, the global positioning systems (GPS) do not work well in buildings and in dense urban areas when there is no lines of sight between the user and their satellites. Hence, the local positioning system (LPS) has been considerably used in recent years. The main purpose of this research is to provide a four-layer artificial neural network based on nonlinear system solver (NLANN) for local pos...
Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. In this study we make an effort to understand pest population dynamics using Neural Netw...
In mountainous basins, snow water equivalent is usually used to evaluate water resources related to snow. In this research, based on the observed data, the snow depth and its water equivalent was studied through application of non-linear regression, artificial neural network as well as optimization of network's parameters with genetic algorithm. To this end, the estimated values by artificial n...
Land use classification and mapping mostly use remotely sensed data. During the past decades, several advanced classification methods such as neural network and support vector machine (SVM) have been developed. In the present study, Landsat TM images with 30m spatial resolution were used to classify land uses through two classification methods including support vector machine and neural network...
During the autumn of 2000, England and Wales experienced the wettest conditions for over 270 years, causing significant flooding. The exceptional combination of a wet spring and autumn provided the potential for soil structural degradation. Soils prone to structural degradation under five common lowland cropping systems (autumn-sown crops, late-harvested crops, field vegetables, orchards and sh...
Background & Objectives: The prediction and quality control of the Karaj River water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, performance of artificial neural network (ANN), combined wavelet-neural network (WANN), and multi linear regression (MLR) models were evaluated to predict next month nitrate and dissolved oxygen of “Pole K...
M. van der Laan1, J.G. Annandale1*, C.C. du Preez2 and S.A. Lorentz3 1 Department of Plant Production and Soil Science, University of Pretoria, Pretoria 0002, South Africa 2Department of Soil, Crop and Climate Sciences, University of the Free State, Bloemfontein 9300, South Africa 3School of Bioresources Engineering and Environmental Hydrology, University of KwaZulu-Natal, Pietermaritzburg 3209...
air pollution is a challenging issue in some of the large cities in developing countries. air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. several methods exist to analyze air quality. among them, we applied the dynamic neural network (tdnn) and radial basis function (rbf) methods to predict the concentrations of ground-level...
In this paper we present several multiple model combination methods, utilizing neural as well as linear predictors, to predict sugar beet crop yield. The results are superior to previous prediction methods which used only neural network or only linear regresison predictors. Abstract. Key Words.
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