Explore the vast range of titles available.

The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject of extensive research. However, a comprehensive understanding of the thermodynamic aspects of dye solution adsorption is lacking. Previous studies have primarily focused on enhancing the adsorption capacity of methylene blue dye. This study aimed to develop an environmentally friendly and cost-effective method for treating methylene blue dye wastewater and to gain insights into the thermodynamics and kinetics of the adsorption process for optimization. An adsorbent with selective methylene blue dye adsorption capabilities was synthesized using rice straw as the precursor. Experimental studies were conducted to investigate the adsorption isotherms and models under various process conditions, aiming to bridge gaps in previous research and enhance the understanding of adsorption mechanisms. Several adsorption isotherm models, including Langmuir, Temkin, Freundlich, and Langmuir–Freundlich, were applied to theoretically describe the adsorption mechanism. Equilibrium thermodynamic results demonstrated that the calculated equilibrium adsorption capacity ( q e ) aligned well with the experimentally obtained data. These findings of the study provide valuable insights into the thermodynamics and kinetics of methylene blue dye adsorption, with potential applications beyond this specific dye type. The utilization of rice straw as an adsorbent material presents a novel and cost-effective approach for MB dye removal from wastewater.

The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

The provision of safe water for people is a human right; historically, a major number of people depend on groundwater as a source of water for their needs, such as agricultural, industrial or human activities. Water resources have recently been affected by organic and/or inorganic contaminants as a result of population growth and increased anthropogenic activity, soil leaching and pollution. Water resource remediation has become a serious environmental concern, since it has a direct impact on many aspects of people’s lives. For decades, the pump-and-treat method has been considered the predominant treatment process for the remediation of contaminated groundwater with organic and inorganic contaminants. On the other side, this technique missed sustainability and the new concept of using renewable energy. Permeable reactive barriers (PRBs) have been implemented as an alternative to conventional pump-and-treat systems for remediating polluted groundwater because of their effectiveness and ease of implementation. In this paper, a review of the importance of groundwater, contamination and biological, physical as well as chemical remediation techniques have been discussed. In this review, the principles of the permeable reactive barrier’s use as a remediation technique have been introduced along with commonly used reactive materials and the recent applications of the permeable reactive barrier in the remediation of different contaminants, such as heavy metals, chlorinated solvents and pesticides. This paper also discusses the characteristics of reactive media and contaminants’ uptake mechanisms. Finally, remediation isotherms, the breakthrough curves and kinetic sorption models are also being presented. It has been found that groundwater could be contaminated by different pollutants and must be remediated to fit human, agricultural and industrial needs. The PRB technique is an efficient treatment process that is an inexpensive alternative for the pump-and-treat procedure and represents a promising technique to treat groundwater pollution.

We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measurements, indicating that the physical and chemical forces governed the removal process.

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Using a thermally stratified water-based nanofluid and a permeable stretching sheet as a simulation environment, this research examines the impact of nanoparticle aggregation on MHD mixed convective stagnation point flow. Nanoparticle aggregation is studied using two modified models: the Krieger–Dougherty and the Maxwell–Bruggeman. The present problem's governing equations were transformed into a solvable mathematical model utilizing legitimate similarity transformations, and numerical solutions were then achieved using shooting with Runge–Kutta Fehlberg (RKF) technique in Mathematica. Equilibrium point flow toward permeable stretching surface is important for the extrusion process because it produces required heat and mass transfer patterns and identifies and clarifies fragmented flow phenomena using diagrams. Nanoparticle volume fraction was shown to have an impact on the solutions' existence range, as well. Alumina and copper nanofluids have better heat transfer properties than regular fluids. The skin friction coefficients and Nusselt number, velocity, temperature profiles for many values of the different parameters were obtained. In addition, the solutions were shown in graphs and tables, and they were explained in detail. A comparison of the current study's results with previous results for a specific instance is undertaken to verify the findings, and excellent agreement between them is observed.

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

Rapid industrialization, urbanization, global warming, and climate change are compromising surface water quality across the globe. Consequently, water conservation is essential for both environmental sustainability and human survival. This study assesses the water quality of the Jamuna River in Bangladesh at five distinct sites during wet and dry seasons. It employs six global water quality indices (WQIs) and contrasts the results with Bangladesh's Environmental Quality Standard (EQS) and the Department of Environment (DoE) criteria. The WQI models used are the Weighted Arithmetic WQI (WAWQI), British Columbia WQI (BCWQI), Canadian Council of Ministers of the Environment WQI (CWQI), Assigned WQI (AWQI), Malaysian WQI (MWQI), and Oregon WQI (OWQI). Fifteen physicochemical parameters were analyzed according to each WQI model's guidelines. The findings reveal that most parameters surpass the standard permissible values. The WQI model results indicate that the average water quality across the five sites falls into the lowest category. A comparison of the WQI models suggests potential correlations between WAWQI and AWQI, as well as between MWQI and OWQI. The straightforward presentation of the WQI models indicates that while the river water requires treatment for household and drinking use, it remains suitable for irrigation. The decline in water quality is likely attributable to human activities, urbanization, municipal waste disposal, and industrial effluents. Authorities must prioritize regular monitoring and assessment of water quality to address the identified challenges. Restoring the water to an acceptable standard will become increasingly difficult without proactive measures.