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Geomorphology generally aims to describe and investigate the processes that lead to the formation of landscapes, while geochronology is needed to detect their timing and duration. Due to restrictions on exporting geological samples from Egypt, modern geoscientific studies in the Nile Delta lack the possibility of dating the investigated sediments and geological features by standard techniques such as OSL or AMS 14C; therefore, this study aims to validate a new approach using machine‐learning algorithms on portable X‐ray fluorescence (pXRF) data. Archaeologically dated sediments from the archaeological excavations of Buto (Tell el‐Fara'in; on‐site) that pXRF analyses have geochemically characterized serve as training data for running and comparing Neural Nets, Random Forests, and single‐decision trees. The established pXRF fingerprints are transferred via machine‐learning algorithms to set up a chronology for undated sediments from sediment cores (i.e., the test data) of the nearby surroundings (off‐site). Neural Nets and Random Forests work fine in dating sediments and deliver the best classification results compared with single‐decision trees, which struggle with outliers and tend to overfit the training data. Furthermore, Random Forests can be modeled faster and are easier to understand than the complex, less transparent Neural Nets. Therefore, Random Forests provide the best algorithm for studies like this. Furthermore, river features east of Kom el‐Gir are dated to pre‐Ptolemaic times (before 332 B.C.) when Kom el‐Gir had possibly not yet been settled. The research in this paper shows the success of close interactions from various scientific disciplines (Geoinformatics, Physical Geography, Archaeology, Ancient History) to decipher landscape evolution in the long‐term‐settled Nile Delta's environs using machine learning. With the approach's design and the possibility of integrating many other geographical/sedimentological methods, this study demonstrates the potential of the methodological approach to be applied in other geoscientific fields. Highlights: Neural Nets and Random Forest yield excellent results in classifying sedimentsC5.0 single‐decision trees showed substantial deficits in this context [ABSTRACT FROM AUTHOR]

In recent years, early detection of hepatitis C virus (HCV) disease has been a vital task in the medical science field. HCV became the main health concern to the public, as it was noticed to have more blood donors in Egypt equated to other nationalities. The WHO assessed that in 2019, around 290 000 individuals died from hepatitis C, which says the seriousness of the HCV disease. So, early prediction, preventions, and curing the disease are vital components to save individuals from HCV. In this paper, we propose and investigate experimental results of the five machine learning (ML) models and probabilistic neural network (PNN) based approach to detect the HCV utilizing University of Califonia Irvine (UCI) ML Egyptian HCV dataset. We also analyze the statistical reports of HCV stages and their features. As per result analysis, the random forest (RF) ML model performs superiorly to other traditional ML algorithms with 97.5% of accuracy. The PNN (incremental hidden layer [HL] neurons) based proposal model shows a very high performance (99.6% of accuracy) at 30‐HL neurons of PNN. As per comparative analysis, the proposed model is superior to experimental basic ML models, and early HCV disease works are related to this area. This research focuses on early detection, prevention, and challenges of handling HCV. [ABSTRACT FROM AUTHOR]

Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ET0) to generate gridded high‐resolution daily ET0 estimates over Egypt. The AI algorithms include support vector machine‐radial basis function (SVM‐RBF), random forest (RF), group method of data handling neural network (GMDH‐NN), multivariate adaptive regression splines (MARS), and dynamic evolving neural fuzzy interference system (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ET0 using FAO56 Penman–Monteith equation as a reference estimate. The multiparameter Kling‐Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replicating FAO56‐PM followed by temperature‐ and mass‐transfer‐based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.76) followed by Caprio (median KGE 0.64), and Penman (median KGE 0.52) equations based on station‐wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. Overall, the RF model performed the best among all the AI models and empirical equations. The generated 0.10° × 0.10° daily estimates of ET0 enabled the detection of a significant increase of 0.12–0.16 mm·decade−1 in the agricultural‐dependent Nile Delta using the modified Mann–Kendall test and Sen's slope estimator. [ABSTRACT FROM AUTHOR]