The Jure landslide that happened in Nepal on August 2, 2014, exemplifies a critical yet understudied type of geohazard: delayed landslides that occur days after peak rainfall. This study investigates the hydro-mechanical triggering mechanism of the Jure landslide by combining field investigations, drone-based aerial surveys, remote sensing interpretation, and coupled seepage-stability numerical modeling. The results reveal that the critical factor in the delayed failure was the confluence area behind the main scarp (1.62 times the landslide’s actual area). This area maintained high groundwater input even after rainfall ceased, leading to progressive saturation and failure 2 days after the peak rainfall. Numerical simulations using five strategic observation points demonstrated that pore water pressure (PWP) increased from −50 kPa to 43.6 kPa within 216 h, reducing the factor of safety (Fs) from 3.5 to 0.80. The landslide ultimately occurred at 194 h with a computed Fs of 0.97, when PWP values at the observation points reached critical levels. The delayed failure mechanism was facilitated by four distinct joint systems in weathered schist bedrock that created preferential flow paths, prolonging the seepage process. This research contributes to the scientific understanding of delayed landslide phenomena by quantifying the relationships among confluence-area hydrology, progressive pore-pressure development, and slope stability deterioration. These findings have significant implications for early warning systems in mountainous regions worldwide, suggesting that monitoring catchment-scale hydrogeological conditions and PWP evolution may be more effective than conventional rainfall-intensity-based systems for predicting delayed landslide failures.

The increasing impacts of climate change on global agriculture necessitate the development of advanced predictive models for efficient water management in crop fields. This study aims to enhance the forecasting of evapotranspiration (ET), potential evapotranspiration (PET), and crop water stress index (CWSI) using state-of-the-art deep learning techniques. This research integrates high-resolution climatic data from the ACCESS-ESM model and incorporates four shared socioeconomic pathways (SSPs) to represent a wide range of future climate scenarios. We employ feed forward neural networks (FFNNs), convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory networks (LSTMs) to predict ET, PET, and CWSI. These findings reveal significant improvements in prediction accuracy, offering valuable insights for agricultural water management in Bangladesh. This approach provides a robust framework for optimizing irrigation practices and enhancing crop resilience against climate-induced water stress.

Vegetation concrete is increasingly being used for slope stabilization in highways, green roofing in urban developments, and erosion control in coastal areas due to its sustainable solutions for enhancing landscape aesthetics, mitigating pollution, and protecting the environment. However, the environmental and economic impacts of different mix proportions, particularly concerning CO2 emissions, remain insufficiently explored. This study aimed to identify the optimal mix proportions of vegetation concrete through laboratory testing that minimize CO2 emissions while ensuring compatibility with plant growth and cost-effectiveness. The vegetation concrete mix were prepared using different quantities of biochar (5 %, 10 %, and 15 %) and cement content (4 %, 8 %, and 12 %) by weight to evaluate their impact on porosity, unconfined compressive strength (UCS), alkalinity, plant compatibility, cost-effectiveness, and CO2 emissions from raw materials. The results indicate that increasing the biochar content from 0 % to 15 % led to a 16 % reduction in porosity and a 92 % increase in UCS of vegetation concrete. Similarly, increasing the cement content from 0 % to 12 % resulted in an 11 % decrease in porosity and a substantial 200 % increase in the UCS of vegetation concrete. Moreover, the addition of 5 % biochar had a beneficial effect on plant growth, however, increasing the biochar content beyond this level adversely impacted plant development. Based on laboratory test results, the recommended optimal mix for vegetation concrete includes a mix of 5 % biochar, 8 % low-alkaline sulfoaluminate cement, 6 % sawdust, and 6 % ferrous sulfate. The analysis of CO2 emission of the materials studied showed that cement contributed the most to CO2 emissions, followed by biochar, sawdust, water, and soil. Notably, the utilization of sulfoaluminate cement led to a 31.8 % reduction in CO2 emissions compared to Portland cement, while also lowering the overall cost of vegetation concrete by 24.7-33.8 %. This research underscores the necessity of scientifically establishing relationships between the composition of plant-based concretes and their environmental and economic performance. In summary, this study identifies optimal mix proportions for vegetation concrete, leveraging low-alkaline sulfoaluminate cement and biochar to minimize CO2 emissions, enhance landscape aesthetics, and ensure compatibility with plant growth, thus emphasizing its potential as a sustainable and cost-effective construction material.

•Presented state-of-the-art regarding sustainable vegetation concrete technology.•Documented recent development of vegetation concrete for slope protection.•Reviewed vegetation concrete’s materials and properties.•Discussed Challenges and future scopes of vegetation concrete.

The novel coronavirus (COVID-19) infectious respiratory disease becomes a global pandemic in few weeks from its start in December 2019 to early 2020. Various countries across the world including China went to lockdown and several caution were implemented to reduce the further spread of this infectious disease. Wuhan (China) was the first city to impose the lockdown for controlling the impact of COVID-19. The lockdown unexpectedly gives the scientific community a chance to investigate the influence of the human activity on air pollution in real world scenario. The present study attempted to investigate the impact of lockdown during the ongoing viral disease on the changes of fine particulate matters and some unhealthy gases i.e. PM2.5, PM10, SO2, CO, O3, AQI and NO2 over Hubei province of China, by using ground station data and TROPOMI satellite data. The air pollutants were compared as, (i) pre COVID-19 period (i.e. October-December 2019), (ii) throughout the lockdown in Hubei province (i.e. January 2020-March 2020) and Post lockdown duration (i.e. April 2020-June 2020). Results clearly showed that air quality was not secured due to high emission of CO, SO2, NO2, O3, PM2.5, and PM10 on Pre COVID-19 times, but under the lockdown continuously decrease in NO2 from (54 mg/cm3 to 26 mg/cm3), SO2 (10.5 mg/cm3 to 7.77 mg/cm3) PM2.5 (49.22 mg/cm3 to 44.34 mg/cm3), PM10 concentrations (80.83 mg/cm3 to 57.04 mg/cm3) and AQI (72.95 mg/cm3 to 49.64 mg/cm3) has been observed. Because lockdown shuts all anthropogenic activities like industrial work, traffic vehicles and various socio-economic activities, which developed a healthy change on air quality. Emission of unhealthy gases and particulates were quite clear during the lockdown but again increase after finishing the lockdown period. However, we don’t support the lockdown as a measure for the betterment of air quality as this has severely posed negative impacts on the socio-economic processes and progress, but changes in human behavior of using industries and vehicles can help us to improve the air quality.

This work developed models to identify optimal spatial distribution of emergency evacuation centers (EECs) such as schools, colleges, hospitals, and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh. The use of location-allocation models (LAMs) for evacuation in regard to flood victims is essential to minimize disaster risk. In the first step, flood susceptibility maps were developed using machine learning models (MLMs), including: Levenberg–Marquardt back propagation (LM-BP) neural network and decision trees (DT) and multi-criteria decision making (MCDM) method. Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic (AUROC) curve. Mathematical approaches in a geographic information system (GIS) for four well-known LAM problems affecting emergency rescue time are proposed: maximal covering location problem (MCLP), the maximize attendance (MA), p-median problem (PMP), and the location set covering problem (LSCP). The results showed that existing EECs were not optimally distributed, and that some areas were not adequately served by EECs (i.e., not all demand points could be reached within a 60-min travel time). We concluded that the proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation.

Along the Karakorum Highway (KKH ), the key route for the China-Pakistan Economic Corridor,there are many rockfalls and unstable slopes,usually caused by tectonic movement and rainfall on the fractured rocks and slopes.This paper presents a numerical investigation of the rockfall and slope stability along the Karakorum Highway in Jijal-Pattan,Northern Pakistan using DIPS,GeoRock 2D and SLIDE,focusing on rockfall and slope stability along the KKH to develop countermeasures.Along the KKH,two maj or sections susceptible to rockfalls were selected to investigate the mechanism of rockfall and slope instability.The stereographic proj ection analysis following four sets of j oints indicates that both sections are prone to plane failure and wedge failure.Based on the limit equilibrium theory,under static loading,the slope for Section 1 showed a stability coefficient of 0.917,representing its instability,and the slope in Section 2 has a stability coefficient of 1.131 depicting its slight stability.However,under the seismic condition,the stability coefficients of the slopes were lower than 1 for both sections,which indicates their instability.The results by GeoRock 2D reveal that in Section 1 the fallen rock mass attained the bounce height of 33 m,and in Section 2 it attained a bounce height of 29 m.The fallen rocks in Section 1 have the total kinetic energy of 1135.099 kJ with a velocity ranging from 0.5 m/s to 44 m/s,while in Section 2 the fallen rocks have a velocity ranging from 0.5 m/s to 40.901 m/s with a damage capacity of 973.012 kJ.This study showed the rockfalls and landslides along the KKH have great damage potential.

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R2 = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

Dam failurecan occur due to multiple reasons like structural instability, hydraulic conditions, and rapid drawdown condition. The rapid drawdown is regarded as the most critical condition for the upstream (u/s) slope.The estimation of the factor of safety for dam slope stabilityis crucial to determine the overall dam safety. This paper deals with the stability analysis for the cut (temporary) and permanent slope of the sedimentation basin area slopes and u/s of intake slopes using Slide v 6.0 and Geo-studio. The properties of materials such as hydraulic conditions, unit weight, cohesion and internal friction angle of soil were measured and then used as input data in the programs to analyze the factor of safety under different cases. The slope stability was analyzed basedon four different Limit Equilibrium methods (e.g. Bishop, Janbu simplified method, Janbu corrected method and Spencer method) while the drawdown condition has been analyzed basedon both Finite Element and Limit Equilibrium methods. The factor of safety values for the sedimentation basin area and for upstream slopes satisfies the minimum limits for all cases of operation. The results achieved from numerical modeling also justified a significant influence on the safety factor of the slope stability of the dam and thus it can be concluded that Neelam dam is safe against the danger of slope failure. The study suggested that the numerical methods provide a relatively easy and fast solution to complex geotechnical problems and must be used during the design stage to avoid possibilities of failure.