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Accurately assessing the erodibility of geomaterials is of great significance for the design of earthen structures and the prevention of the associated failure induced by seepage force. Recently, the un-resolved Computational Fluid Dynamics–Discrete Element Method (CFD–DEM) has been widely used to investigate internal erosion. However, due to the use of wall boundary and the fact that the fixed CFD domain cannot be changed with the soil sample’s volume contraction during the erosion test, a larger porosity at the boundary of the CFD domain is commonly formed, resulting in sidewall preferential flow (i.e., relatively more fine particles migrate along the boundary of the DEM domain) and thereby overestimating the soil erodibility. In this study, a new method based on particle boundary is developed to tackle this problem. The newly proposed particle boundary can prevent its particles from erosion via inter-particle bonding and transfer stress from servo walls to the simulated sample. An optimal particle boundary thickness is determined by considering sample contraction and computational efficiency. The performance of the new method was compared with the conventional method and also verified using experimental results. The results show that the newly proposed method has significantly improved the uniformity of fluid velocity distribution. Furthermore, the cumulative eroded mass of fine particles in the new model is approximately 15% lower than in the conventional model. It is convincingly demonstrated that the new method can simulate internal erosion better and give a more accurate assessment of geomaterial erodibility.

Persistence expressed by hurst exponent (H) is estimated for streamflows of 122 stations in the basins of Peninsular India by three methods at different aggregation scales (daily, monthly mean, monthly and annual maximum). Mean H values indicated long term persistence (LTP) and the data of more than 70% stations showed LTP for other temporal resolutions. H displayed negative correlation with time series length, mean annual and specific mean discharges, while no significant correlation with catchment area. Positive dependence was found between persistence of streamflow and different climatic attributes (rainfall; maximum, mean and minimum temperature) for daily and annual maximum datasets.

The existence of an upper limit for extremes of quantities in the earth sciences, e.g. for river discharge or wind speed, is sometimes suggested. Estimated parameters in extreme-value distributions can assist in interpreting the behaviour of the system. Using simulation, this study investigated how sample size influences the results of statistical tests and related interpretations. Commonly used estimation techniques (maximum likelihood and probability-weighted moments) were employed in a case study; the results were applied in judging time series of annual maximum river flow from two stations on the same river, but with different lengths of observation records. The results revealed that sample size is crucial for determining the existence of an upper bound.

The changes in the river stretch by engineering constructions such as bridges affect its physical attributes and imply an impact on biological functioning. Thus, the ecological behavior of the river devises a scope for the future study correlating the probable flood risk areas. The alteration of river habitat and the riparian environment have been investigated in the present study using two methods: Habitat Modification Score and Fluvial Functioning Index. The results have further considered scouting with the risk zonation map that signifies the sensitive affected zone during the high discharge phase. The study displays the impact of human intervention that shows a better river habitat condition and healthy riparian environment. It is distinctly noted along the studied reach that, the bridge construction has modified the river channel showing poor functioning level and habitat condition. The flood risk-zoning map too highlights the areas with human interventions that are likely to get affected by high flood occurrences than the least intervened zones. Hence, the study offers an insight into the bridge constructions’ ascendancy on the river habitat condition and flood risk zone.

Establishment of the relationship between soil suction and water content, commonly termed as soil–water characteristic curve (SWCC), is of prime importance in the field of unsaturated soil mechanics. There are several instruments available that can be used to measure the SWCC of soil, but every suction measuring device has its own limitations in terms of its suction measurement range. Therefore, the preciseness of the estimated unsaturated soil properties largely depends on the range of suction measurements and the type of instruments used. The primary objective of this study is to quantify the error that can occur during the estimation of unsaturated hydraulic conductivity function (UHCF) from SWCC for low plastic soils. Experiments were performed to investigate the influence of different suction measurement devices on the estimated UHCF for four different soils with varying clay content. A dew point potentiometer (WP4) and a miniature tensiometer (T5) have been used in this study for the suction measurement. The SWCC of the selected soils were predicted mathematically using a commonly used pedo-transfer function (PTF). The experimental results clearly indicated that the sole use of WP4 overestimated the SWCC parameters, as well as UHCF (overestimation in the conductivity value is in order of 104 times). Rather, a combination of T5 and WP4 data, within their accurate range, provides a more precise estimation of UHCF. Further, the accuracy of the PTF was found very effective for low plastic soils with a relatively low percentage of clay (% clay < 10), in the absence of any experimental data.

Analyzing well-testing data by the type-curve matching is a modern well-testing analysis method and is widely used in the petroleum and gas industry. By improving accuracy of type curve, we can get more accurate results from analyzing well-testing data, which provide a scientific base for development of oil, gas and water resources. By solving percolation equations, we can obtain type curves. The Laplace transformation methods are often used to solve them. In this paper, we improve the accuracy of type curve by improving the numerical inverse Laplace transformation (NILT) based on infinite series. We combine the NILT based on infinite series with Levin convergence acceleration and determine necessary parameters through numerical experiments to improve accuracy and speed. To verify this method, we compare the improved method with the Stehfest method using some functions such as trigonometric function. Type curves for analysis of well-testing data for the homogeneous reservoir with elastic outer boundary and a dual porosity reservoir are plotted and compared by using the improved numerical inversion and the Stehfest numerical inversion, respectively. These results show that type curves plotted by the improved method are less in vibration and fluctuation than ones plotted by the Stehfest method.

In the current era of computing, communication, and technology, hydrological, metrological, and geographical parameters supported by sensor-based systems are available to detect, monitor, and analyze natural disasters like landslides. The landslide-related information from the study area is collected in offline mode through site visits. This process of collecting data in offline mode may cause delays in prediction and proactive decision-making in real-time mode. Although landslides cannot be prevented, their impact on human life and the environment can be reduced through real-time monitoring and prediction using IoT, Cloud, and Machine Learning Technologies. This manuscript aims to present a robust, real-time monitoring system that can minimize losses and save lives. The proposed model utilizes Internet of Things (IoT) technology integrated with cloud services to monitor and analyze landslides in a specific study area. The real-time monitoring system relies on three types of parameters: hydrological, meteorological, and geographical. These parameters are used to collect and store real-time information in an IoT cloud platform. The IoT cloud information is fetched on the LANDSLIDE MONITOR application for proactive decisions. To predict landslide events in areas prone to disasters, supervised learning classifiers were employed. The prediction analysis takes into account meteorological, hydrological, and geographical factors. The effectiveness of the proposed real-time landslide monitoring system was tested and evaluated in the Varunavat hills of the Uttarkashi District in Uttarakhand, India. The performance of the system was assessed by analyzing the accuracy of the model at different levels. The major focus of the developed system includes real-time data storage of landslide-prone areas in the IoT cloud, predictive modeling, and lastly the real-time landslide responses on LANDSLIDE MONITOR. The present landslide monitoring system uses long short-term memory networks (LSTM) and gated recurrent units (GRU) for predictive modeling of the landslide events in the study area. Hence the unique contribution of the work includes the technologies integration and real-time data collection from the study area and stored in the IoT cloud. The novel contribution of the work also includes the predictive modeling of landslide events using LSTM and GRU, and study area people awareness using LANDSLIDE MONITOR for level of risk from landslide events. The accuracy rates for the alert classes, ‘no threat’, ‘mild threat’, and ‘high threat’ events are 96.83%, 97.07%, and 98.56%, respectively. With a mean F 1 score of 0.96 across the three classes of landslide occurrences, the proposed system demonstrates a high level of accuracy.

It is widely recognized that soil microorganisms undergo adaptation in response to phosphorus (P)-depleted tropical soils by enhancing the abundance of phosphatases, as evidenced by an increase in the maximum rate of substrate conversion ( V max ) of an assemblage of phosphatases. Conversely, the question remained unclear as to whether soil microorganisms adapt to P-poor conditions by producing “high-quality” enzymes, characterized by an increased affinity in the produced phosphatases, as indicated by a lower Michaelis constant ( K m ). Through an integrated analysis that encompasses both previously published data from 10-year P-fertilized forests and newly acquired data from a eucalyptus-dominated planted forest in a 6-year P-fertilized forest, we have demonstrated that soil microorganisms adapt to P-deficient conditions by increasing V max , rather than by producing high-quality phosphatases (phosphomonoesterases). In response to this, we have proposed a novel hypothesis, termed \"the enzyme degradation hypothesis,\" which effectively elucidates why microorganisms prioritize quantity over quality of phosphatases. Producing a small quantity of high-quality phosphatases is less advantageous, as proteolytic degradation has a greater impact in this strategy compared to producing a large quantity of low-quality phosphatases. This is because, as the availability of phosphatases—the substrate for proteases—decreases, the proportion of degraded phosphatases relative to the total phosphatase pool increases, due to the upward convexity of the enzyme reaction described by the Michaelis–Menten equation. This hypothesis requires further validation in other forest ecosystems, including different types of tropical forests.

The Australian Bureau of Meteorology has developed a national hydrological projections (NHP) service for Australia. The NHP aimed to provide nationally consistent hydrological projections across jurisdictional boundaries to support planning of water-dependent industries. NHP is complementary to those previously produced by federal and state governments, universities, and other organisations for limited geographical domains. The projections comprise an ensemble of application-ready bias-corrected climate model data, derived hydrological projections at daily temporal and 0.05° × 0.05° spatial resolution for the period 1960–2099, and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5). The spatial resolution of the projections matches that of gridded historical reference data used to perform the bias correction and the Bureau of Meteorology's operational gridded hydrological model. Three bias correction techniques were applied to four CMIP5 global climate models (GCMs), and one method was applied to a regional climate model (RCM) forced by the same four GCMs, resulting in a 16-member ensemble of bias-corrected GCM data for each emission scenario. The bias correction was applied to fields of precipitation, minimum and maximum temperature, downwelling shortwave radiation, and surface winds. These variables are required inputs to the Bureau of Meteorology's landscape water balance hydrological model (AWRA-L), which was forced using the bias-corrected GCM and RCM data to produce a 16-member ensemble of hydrological output. The hydrological output variables include root zone soil moisture (moisture in the top 1 m soil layer), potential evapotranspiration, and runoff. Here we present an overview of the production of the hydrological projections, including GCM selection, bias correction methods and their evaluation, technical aspects of their implementation, and examples of analysis performed to construct the NHP service. The data are publicly available on the National Computing Infrastructure (10.25914/6130680dc5a51, Bureau of Meteorology, 2021), and a user interface is accessible at https://awo.bom.gov.au/products/projection/ (last access: 24 November 2023).

Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO’s efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM’s good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.