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The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city's urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm. [ABSTRACT FROM AUTHOR]

This study explores the relationship between Land Use Land Cover (LULC), Land Surface Temperature (LST), and Urban Heat Island (UHI) in Dakahlia Government using Landsat 8 images from 2014 to 2020. Support Vector Machine (SVM) and Mono-Window Algorithm were used to generate LULC and estimate LST. Results reveal an increase in built-up areas, rising LST, and variable UHI thresholds. The study highlights the impact of COVID-19 on LST in 2020. Positive correlations between LST and Normalized difference build-up index (NDBI) and negative correlations with Normalized difference vegetation index (NDVI) were observed. Projections for 2030 suggest an increase in high-temperature areas. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is essential for quick and accurate Global Navigation Satellite System GNSS location and navigation. In addition to location parameters, there are various additional GNSS characteristics that are relevant for a wide range of applications such as instrumental calibrations, atmospheric sounding, and time transfer. We offer differential code bias and satellite geometry for the GNSS estimable parameters using MANS-PPP software backage. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. We demonstrated how differential code bias and satellite geometry can effectively enhance initial time and positioning error for multi-GNSS satellites. PPP Processing observation data in static mode was used by the different DCB files the Chinese Academy of Sciences (CAS), the German Aerospace Centre (DLR), and the Centre for Orbit Determination in Europe (CODE), for the 12 stations from IGS, and we analyzed the impact of errors from the satellite geometry. The results illustration that the correction of DCB significantly improves the PPP ambiguity resolution success rate and quality, which have higher DCB values. The satellite geometry also has a substantial influence on the PPP ambiguity resolution, with a better geometry leading to a higher success rate and quality. Furthermore, the use of multiple GNSS constellations and the optimization of the satellite selection and weighting algorithms can further improve the PPP ambiguity resolution and the resulting positioning accuracy. [ABSTRACT FROM AUTHOR]

One of the primary geodetic mapping tasks in the post-processing of GNSS data is precise point positioning (PPP). Research institutions and universities have established software packages and online PPP services in prior years. Still, it is problematic to satisfy the high-rate update criterion of PPP due to the quick growth of GNSS constellations. In PPP GNSS data processing, Square Root Information Filter SRIF is not frequently handled. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. The new program has been demonstrated how can effectively enhance initial time and positioning error for multi-GNSS satellites. Processing observation data with the Kalman filter and SRIF was performed using PPP in static mode for the 16 stations, and the influence of errors has been analyzed from the filtering method. The Kalman filter was unable to maintain a stable convergence curve during the PPP filtering procedure, but SRIF was successful in doing so. Based on these findings, SRIF had better numerical stability and was well-suited for settings with PPP demanding precision computing environments. [ABSTRACT FROM AUTHOR]

It is critical to evaluate the reliability of using free online processing software for Global Navigation Satellite System post-processing since the advancement of GNSS gives satellite navigation customers all over the world great benefits. AUSPOS, IBGE, Magic GNSS, CSRS-PPP, and open-source software are some of the online processing solutions being evaluated for accuracy in this study. GNSS observations from IGS were used to conduct field observations using RINEX 3 data from three stations, with an observation duration of 30 seconds. After data acquisition, it was processed with online and free software that made use of GPS, GPS, and GLONASS. When compared to coordinates obtained from reference stations, the relative differences and correctness of the coordinates generated by each piece of software were then evaluated. coordinates in the ITRF14 reference for the X, Y, and Z directions. Online GNSS processing services may be used with greater accuracy for engineering and geodetic applications and are simple to use without the need for GNSS data processing expertise. [ABSTRACT FROM AUTHOR]

This study investigates the use of new machine learning techniques in mapping variation in ground levels based on ordinary spirit levelling (SL) measurements. Convolution Neural Network (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and bi-directional LSTM (BILSTM) were developed and compared in the current study to estimate the leveling through SL measurements. SL measurements of the Manzalla region, Egypt, were used in the current study. 3253 datasets of SL observation points, including 229 benchmarks of precise levelling (PL), were used to design and verify the proposed model's results. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The root mean square error and correlation determination of the LSTM model are 7.4 cm and 0.99, respectively, in the testing stage. The accuracy of mapping ground levelling through the developed LSTM model is close to 99% in terms of model error. [ABSTRACT FROM AUTHOR]

The unconfined compressive strength (UCS) of rocks is one of the most important properties used in rock science and engineering practices. Thus, many researchers attempt to develop computational intelligence models for estimating the UCS of different types of rocks. This study develops and compares eight models to estimate the rock UCS. Six hybrid ANNs were constructed using artificial bee colony, ant colony optimization, antlion optimizer, imperialist competitive algorithm, shuffled complex evolution, and teaching learning-based optimization algorithms. Additionally, two regression-based computational models, namely multivariate adaptive regression splines (MARS) and Gaussian process regression (GPR), were utilized. The experimental datasets at different locations in Malaysia and Iran, including the UCS and other rock indices, were collected and used in this work. Experimental findings indicate that the performances of all models are adequate for UCS estimation, with R2 values scattered in the range of 0.87 to 0.97 and 0.94 to 0.98 in the training and testing phases, respectively. However, the developed MARS model was ranked high compared to other models, including GPR and hybrid ANNs, and the RMSE of the MARS model was found to be 2.27 MPa. Overall, the developed MARS model outperformed other employed models based on the R2 and RMSE criteria and can be utilized precisely for the UCS estimate of rocks. [ABSTRACT FROM AUTHOR]

Gravity meters can be used to measure all effects that make up the Earth's gravity field. Many of these effects are caused by known sources such as the Earth's rotation, distance from the Earth's center, topographic relief, and tidal variation. Physical fields are the main component of many centuries of the paradigm of all of Earth Sciences. The changing gravitational field is very important subject of research in the scientific aspect and practical. This paper applies two analytical models to simplify the gravitation calculation, which are sphere and cone models. Examples and finite element applications for the two models are studied also and discussed. The results of this study reveal that the possibility of using the proposed method with using presented analytical, finite element and numerical models to estimate the better determination of the characteristics of the local gravity of natural and man-made objects of sizes up to several tens of kilometers. [ABSTRACT FROM AUTHOR]

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months or even years. This paper investigates the performance of several soft computing techniques for point displacement modeling using GNSS technique during the process of monitoring the structural deformation of suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the availability of a large amount of positions data generated from GNSS data positions for monitoring the deformation of such structure, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS positions and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ± 2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods, and therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements. [ABSTRACT FROM AUTHOR]

Bridges are playing a major role in the socioeconomic development of any country over the world. Suspension bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these types of structures is important and vital. Time of observations for the purpose of structural deformation can vary from a few hours, days to several months, or even years. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and points displacements prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity, and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive soft computing model for processing GNSS observations and points displacement prediction. Several mathematical models and two cases of data amount (66.67% and 50% of all available data) for dynamic and kinematic state are applied and compared for prediction of suspension bridge displacement with confidence interval with a probability ρ = 0.95, Δ = ±2σ. The resulting point displacement values by applying ANNs and ANFIS, which used a confidence interval with a probability of ρ = 0.95, Δ = ± 2σ when using 66.67% of all data, are more accurate and reliable than any other applied methods; therefore, ANNs and ANFIS can provide a significant improvement of understanding and predicting the structure deformation values where conventional mathematical modeling techniques were not as accurate or capable especially in dynamic prediction of displacements. [ABSTRACT FROM AUTHOR]

The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future. [ABSTRACT FROM AUTHOR]

This study presents multi input–single output (MISO) soft computing models for forecasting the significant wave height (Hs) of lakes and investigates the impact of input variables for designing the prediction models. Wind direction and speed, air and water temperature changes, wave period and direction, and sea level pressure variables are used as input parameters for the model design to predict the Hs of Michigan Lake. The M5 regression tree (M5T) and robust regression (RR) are used to study the effectiveness of input parameters that impact Hs prediction model. Furthermore, four MISO soft computing models, namely regression (RR and M5T), artificial neural network (ANN), and least square support vector machine (LSSVM), are evaluated and compared to estimate the best model for predicting the Hs. The results show that the wave's period variable has high impact for prediction of lake Hs values, while the other variables have the same impacts on the prediction accuracy of Hs. The comparison between the developed models shows that the four model performances are acceptable in the training stage, while the MISO-ANN model gives better precision for the prediction of Hs of the lake in the testing stage. [ABSTRACT FROM AUTHOR]