Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector functional link (dRVFL), general regression neural network (GRNN), multivariate adaptive regression spline (MARS), online sequential extreme learning machine (OSELM) and extreme gradient boosting decision tree (XGBoost) when compared with observed river salinity data. Also, the KELM‐BSSADE model effectively identified optimal inputs through the Boruta‐XGBoost (B‐XGB) feature selection method. Four metaheuristic‐based KELM models were developed, utilizing grey wolf optimizer, whale optimization, slime mould algorithm and equilibrium optimizer, further illustrating the capability of KELM‐BSSADE in estimating potential salinity in river water. By accurately estimating potential salinity, KELM‐BSSADE can assist in optimizing irrigation practices, ensuring that agricultural demands are met while minimizing the risk of salinity‐related crop damage.
Nanosilica was extracted from rice husk, which was locally collected from the Iraqi mill at Al-Mishikhab district in Najaf Governorate, Iraq. The precipitation method was used to prepared Nanosilica powder from rice husk ash, after treating it thermally at 700°C, followed by dissolving the silica in the alkaline solution and getting a sodium silicate solution. Two samples of the final solution were collected to study the effect of filtration on the purity of the sample by X-ray fluorescence spectrometry (XRF). The result shows that the filtered samples have purity above while the non-filtered sample purity was around The structure analysis investigated by the X-ray diffraction (XRD), found that the Nanosilica powder has an amorphous
... Show MoreThe Enhanced Thematic Mapper Plus (ETM+) that loaded onboard the Landsat-7 satellite was launched on 15 April 1999. After 4 years, the image collected by this sensor was greatly impacted by the failure of the system’s Scan Line Corrector (SLC), a radiometry error.The median filter is one of the basic building blocks in many image processing situations. Digital images are often distorted by impulse noise due to errors generated by the noise sensor, errors that occur during the conversion of signals from analog-to-digital, as well as errors generated in communication channels. This error inevitably leads to a change in the intensity of some pixels, while some pixels remain unchanged. To remove impulse noise and improve the quality of the
... Show MoreThe goal of this research is to develop a numerical model that can be used to simulate the sedimentation process under two scenarios: first, the flocculation unit is on duty, and second, the flocculation unit is out of commission. The general equation of flow and sediment transport were solved using the finite difference method, then coded using Matlab software. The result of this study was: the difference in removal efficiency between the coded model and operational model for each particle size dataset was very close, with a difference value of +3.01%, indicating that the model can be used to predict the removal efficiency of a rectangular sedimentation basin. The study also revealed
In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.
In this paper, we designed a new efficient stream cipher cryptosystem that depend on a chaotic map to encrypt (decrypt) different types of digital images. The designed encryption system passed all basic efficiency criteria (like Randomness, MSE, PSNR, Histogram Analysis, and Key Space) that were applied to the key extracted from the random generator as well as to the digital images after completing the encryption process.
Study of group action of stone columns using FEM
Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essentia
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