•Merge the advantages of the Wavelet, ARIMA and ANN for drought forecasting.•Propose new model structure using the time series of SIAP and SPI.•High and low frequency sub-series were passed through ...ANN and ARIMA respectively.•Comparison between parametric SPI drought index with non-parametric SIAP.•Possibility of using this model in flood forecasting study.
Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.
The incorporation of waste materials generated in many industries has been actively advocated for in the construction industry, since they have the capacity to lessen the pollution on dumpsites, ...mitigate environmental resource consumption, and establish a sustainable environment. This research has been conducted to determine the influence of different rice husk ash (RHA) concentrations on the fresh and mechanical properties of high-strength concrete. RHA was employed to partially replace the cement at 5%, 10%, 15%, and 20% by weight. Fresh properties, such as slump, compacting factor, density, and surface absorption, were determined. In contrast, its mechanical properties, such as compressive strength, splitting tensile strength and flexural strength, were assessed after 7, 28, and 60 days. In addition, the microstructural evaluation, initial surface absorption test, = environmental impact, and cost-benefit analysis were evaluated. The results show that the incorporation of RHA reduces the workability of fresh mixes, while enhancing their compressive, splitting, and flexural strength up to 7.16%, 7.03%, and 3.82%, respectively. Moreover, incorporating 10% of RHA provides the highest compressive strength, splitting tensile, and flexural strength, with an improved initial surface absorption and microstructural evaluation and greater eco-strength efficiencies. Finally, a relatively lower CO
-eq (equivalent to kg CO
) per MPa for RHA concrete indicates the significant positive impact due to the reduced Global Warming Potential (GWP). Thus, the current findings demonstrated that RHA can be used in the concrete industry as a possible revenue source for developing sustainable concretes with high performance.
•A critical review based on 142 articles on the web of science.•The performances of various models are evaluated.•The modeling best practices are discussed.•Several potential research opportunities ...are recommended.
Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
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Groundwater is a predominant stream of potable water in numerous areas and frequently harbors pollutant removal, notably iron, and manganese. The present work explored synthesizing and conducting a ...thorough analysis of a composite material termed nanometal oxide-biochar (NMO) and biochar that is prepared from date palm waste. The application of Fourier Transform Infrared (FTIR) spectroscopy analysis, SEM/EDX, XRD, and BET facilitated the identification of unique molecule characteristics inside the composite material. This research also investigated the kinetics of manganese and iron adsorption, and the results suggested that both first- and second-order models are applicable, with a slight preference for the pseudo-second-order model. The mechanisms of adsorption in the NMO were further clarified by the Langmuir and Freundlich adsorption isotherm models, which emphasized that the NMO predominantly undergoes monolayer adsorption. In short, composite materials exhibited an adsorption capacity of 3.169 mg/g and 4.151 mg/g for manganese and iron on biochar as well as 4.33 mg/g and 4.859 mg/g on NMO, respectively. In addition, values for R2 provide goodness of fit for the Adams–Bohart and Thomas models. The adsorption capacity for manganese and iron are observed as 31.97 mg/g and 32.28 mg/g on NMO as well as 26.6 mg·L−1 and 29.54 mg·L−1 on biochar, respectively, at a flow rate of 7 mL/min. In conclusion, this study highlights the potential of the NMO-BC composite for monitoring water pollution, sustainably obtained from date palm waste, as a viable approach for eliminating manganese and iron from polluted water.
There has been a steady growth in the length of pipelines over the past 45 years, with over 6000 operating platforms extracting oil. Several facilities would reach their operational life, which can ...no longer be economically viable for their production and will eventually undergo the decommissioning procedure. Almost 3000 petroleum industries will likely be decommissioned worldwide in the next 17 years. By 2030, the total cost of decommissioning globally amounted to about USD 104.5 billion. The choice to decommission the offshore oil and gas sector is considered complicated and crucial as it must evaluate numerous variables such as cost, health and safety, and environmental consequences. This review paper aims to assess the decommissioning activity, specifically on pipelines in the oil and gas industry. The purpose of this study is to understand and evaluate significant environmental impacts associated with decommissioning of oil pipelines and to propose mitigation measures to address the challenges of decommissioning. Waste disposal, a threat to biodiversity and air pollution, is a major environmental concern in decommissioning oil and gas pipelines. Among the decommissioning measures, leave in-situ has the lowest environmental impact while repurposing and recycling, with the application of environmental impact qualitatively and quantitatively by integrating 3D information models, mathematical models embedded in hydrodynamic models look promising for decommissioning.
This research goal is to evaluate the characteristics of glass powder (GP), quartz powder (QP), and limestone powder (LP) as Supplementary Cementitious Materials (SCMs) to replace cement content in ...terms of fresh and hardened properties of Self-Compacting Concrete (SCC) for sustainable building construction. Moreover, the obtained results were modeled using a soft computing approach. This investigation created ten mixtures incorporating varying percentages of GP, QP, and LP by replacing cement at about 0 %, 10 %, 20 %, and 30 %, respectively. The slump flow and J-ring tests were done to observe how SCMs affected the properties in fresh condition. In addition, the mechanical properties and pore structure configuration of the specimens were investigated. It was observed that GP and LP positively affected the fresh properties, increasing the mixes flowability by up to 8 %. Moreover, 20 % GP was able to enhance the compressive strength by 7 % by improving the pore structure of the cement matrix, which was confirmed by the mercury intrusion porosimetry analysis. Finally, the built machine learning models indicated good accord with test outcomes for Artificial Neural Network (R2 = 0.95) and could be applied to calculate the compressive strength of concrete containing GP, QP and LP for construction housing sector.
This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid ...nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.
This research explored the potential of marble wastewater (MWW) in cement paste and mortar production, addressing water scarcity, sustainable growth, and resource management. It investigated the ...physico-mechanical properties and microstructure of cement materials incorporated with varying amounts of MWW. In this study, we utilized tap water and MWW for mortar quality testing, focusing on parameters including setting times, water absorption, and mechanical strength. The viability of MWW in concrete formulations was confirmed by its acceptable total dissolved solids and alkalinity levels. A comprehensive experimental program determined that using marble wastewater in place of tap water reduced the quantity of water required for cement consistency and generated slightly higher compressive strengths (2, 3, 4, and 6%) after 28 days of curing. Analytical techniques, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray analysis, and X-ray diffraction (XRD), were employed for molecular and microstructural analyses, which revealed that MWW had a significant influence on portlandite development and CSH formation at higher replacement levels. In short, this research highlights the feasibility of using MWW in cement products, contributing to sustainable water resources, and industrial waste management and utilization.
Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron ...neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training.
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for ...Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.
