The rapid increase in the population of many of the older major cities within the countries of the Saharan-Arabian Desert is steering vast and disorganized urban expansion and in many cases introducing adverse environmental impacts such as soil erosion, rise in groundwater levels, and contamination of shallow aquifers, as well as development of deformational features including land subsidence. Using the rapidly growing city of Riyadh (1992: 467 km2; 2018: 980 km2), the capital of the Kingdom of Saudi Arabia as a test site, we utilized Small Baseline Subset (SBAS) interferometric analyses of 2016 to 2018 Sentinel-1 images together with multi-temporal high-resolution images viewable on Google Earth, GPS, field, land use land cover (LULC), and geological data to assess the distribution and rates of land subsidence and their causal effects. Three main causes of subsidence were identified and assessed: (1) discharge of wastewater effluents from septic systems in newly urbanized areas that lead to an increase in soil moisture, rise in groundwater levels, waterlogging, and wetting and hydrocompaction of dry alluvium loose sediments causing land subsidence (up to −20 mm/y) in wadis and lowlands; (2) the subsurface dissolution of karst formation by wastewater effluents and the collapse of voids and cavities at depth under stresses introduced by heavy construction machinery, causing sagging and land subsidence (up to −5 mm/y); and (3) leveling, compaction, and degradation of municipal and building waste materials in organized landfills and disorganized dump sites that resulted in significant land subsidence (up to −21 mm/y) and differential settling that could jeopardize the stability of structures erected over these sites. Our findings highlight the potential use of the advocated integrated approach to assess the nature and extent of land deformation associated with rapid urban growth in arid lands, and to identify areas most impacted for the purpose of directing and prioritizing remediation efforts.

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In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

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Pressure vessel plays an important role in wide range of applications to store gas or liquid substances. In order to design a pressure vessel safely, one of main factors which has to be considered is selection of proper burst pressure perdition criterion. Due to large range of available materials in manufacturing of the vessels under different working conditions, several criteria to forecast burst pressure of the vessels have been developed and used by designers. Choosing the most proper criterion based on working condition and the material is a vital task to meet design requirements because inappropriate criterion may lead to unsafe vessel or over design. This issue makes not only pressure vessel design more complex but also maintenance planning, especially for designers who do not have enough experience, is a challenging task. Therefore, lack of a burst pressure predictor model which is able to determine the pressure more accurately for wide range of materials and applications has been remained unsolved. To evaluate machine learning techniques in prediction of burst pressure of pressure vessels, in this paper, a new model based on artificial neural network (ANN) has been proposed and developed. Input parameters of the model includes inner and outer diameter, ultimate and yield strength; output is burst pressure. The obtained results showed that the constructed model has a good potential to be used as more applicable model compared to current models in design of pressure vessels.