Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that

     The aim of the study is to investigate the effects of space weather on the troposphere, where our climate exists. This work is useful to give us an idea of ​​the interaction between solar activity and some meteorological parameters. The sunspot number (SSN) data were extracted from the World Data Center for the production, preservation, and dissemination of the international sunspot number (SILSO), top net solar radiation (TSR) and temperature 2 meters from the ERA5 model of the Copernicus Climate Change Service (C3S) from the Climate Data Store with 0.25 grid Resolution, providing a rich source of climate data for researchers. This study was conducted from 2008 to 2021 (solar cycle 24 and the beginning of 25) over Iraq loca

      Electrical resistivity methods are one of the powerful methods for the detection and evaluation of shallower geophysical properties. This method was carried out at Hit area, western Iraq, in two stages; the first stage involved the use of 1Dimensional Vertical Electrical Sounding (VES) technique in three stations using Schlumberger array with maximum current electrodes of 50m. The second stage included the employment of two dimension (2D) resistivity imaging technique using dipole-dipole array with a-spacing of 4m and n-factor of 6 in two stations. The 1D survey showed good results in delineating contaminated and clear zones that have high resistivity contrast. Near the main contaminated spring, the 2D resi

The resistivity survey was carried out by using vertical electrical sounding (VES) and 2D imaging techniques in the northern Badra area, Eastern Iraq. Eleven VES points distributed on two parallel profiles and six 2D imaging stations were applied using long survey lines.
In general, two types of aquifers are recognized in the study area. The first is the Quaternary aquifer, which appears in all geological sections and inverse model of 2D imaging stations (2DS).This aquifer can be divided into upper and lower aquifers as shown in (2DS1), (2DS3), and (2DS4). Generally, the thickness of this aquifer ranges between (30-200 m) which occurs at a depth of (10-30m) according to geological sections, while its thickness ranges between (35-180m)

     Machine learning-based techniques are used widely for the classification of images into various categories. The advancement of Convolutional Neural Network (CNN) affects the field of computer vision on a large scale. It has been applied to classify and localize objects in images. Among the fields of applications of CNN, it has been applied to understand huge unstructured astronomical data being collected every second. Galaxies have diverse and complex shapes and their morphology carries fundamental information about the whole universe. Studying these galaxies has been a tremendous task for the researchers around the world. Researchers have already applied some basic CNN models to predict the morphological classes