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Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. Two Well-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results.

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BODeff), chemical oxygen demand (CODeff) and total nitrogen (TNeff). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BODeff, the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both CODeff and TNeff in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz. , Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz. , F − , Cl − , NO 2 − , NO 3 − , and SO 4 2− were measured by ion chromatograph. The physicochemical parameters viz. , temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl − , SO 4 2− , and Cr concentrations while textile dyeing effluents have higher pH, NO 2 − , and NO 3 − concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

In this study, observations were carried out in the surface waters of Saraydüzü Dam Lake within Sinop provincial borders for 1 year to determine water quality. The basic 28 variables used to determine water quality were measured monthly at six stations. Taking into account the World Health Organization's drinking water standards, the water quality index (WQI) and Turkey’s Ministry of Forestry and Water Affairs Surface Water Quality Regulations (SWQR) were used in determining the water quality. In addition, irrigation water quality was examined. For this, sodium absorption rates (SAR), sodium percentage and residual sodium carbonate (RSC) values were calculated. WQI values in the lake were found to be between 17.62 and 29.88. Water quality parameters did not exceed the recommended limit values in all months and at all stations. According to these values, the Saraydüzü Dam Lake water belongs to the ‘very good’ class in terms of drinking water quality. The results obtained showed that there were no nitrogen or phosphate inputs that could harm the ecosystem in the lake and that there were no low/insufficient ambient oxygen conditions resulting from excessive oxygen consumption during the degradation process of organic matter. All water quality parametres are well below the permissible limits except some heavy metals according to SWQR. Cu, Zn and Fe were found to exceed the limit values. The water quality of irrigation water was found to be good in terms of SAR and sodium percentage, whereas RSC was observed to have varying qualities during the year and not be suitable for irrigation in some months. According to results of factor analysis (FA), pH, temperature, electrical conductivity, suspended solid matter (SSM), biological oxygen demand (BOD), total hardness (TH),total alkalinity (TA), calcium, nitrate, ammonium, mercury and dissolved oxygen are the main variables responsible for the processes in the ecosystem.

Maintaining good surface water quality is crucial to protect ecosystem health and for safeguarding human water use activities. However, our quantitative understanding of surface water quality is mostly predicated upon observations at monitoring stations that are highly limited in space and fragmented across time. Physical models based upon pollutant emissions and subsequent routing through the hydrological network provide opportunities to overcome these shortcomings. To this end, we have developed the dynamical surface water quality model (DynQual) for simulating water temperature (Tw) and concentrations of total dissolved solids (TDS), biological oxygen demand (BOD) and fecal coliform (FC) with a daily time step and at 5 arcmin (∼ 10 km) spatial resolution. Here, we describe the main components of this new global surface water quality model and evaluate model performance against in situ water quality observations. Furthermore, we describe both the spatial patterns and temporal trends in TDS, BOD and FC concentrations for the period 1980–2019, and we also attribute the dominant contributing sectors to surface water pollution. Modelled output indicates that multi-pollutant hotspots are especially prevalent across northern India and eastern China but that surface water quality issues exist across all world regions. Trends towards water quality deterioration have been most profound in the developing world, particularly sub-Saharan Africa and South Asia. The model code is available open source (10.5281/zenodo.7932317, Jones et al., 2023), and we provide global datasets of simulated hydrology, Tw, TDS, BOD and FC at 5 arcmin resolution with a monthly time step (10.5281/zenodo.7139222, Jones et al., 2022b). These data have the potential to inform assessments in a broad range of fields, including ecological, human health and water scarcity studies.

The measurement of the wastewater BOD5 level requires five days, and the use of a prediction model to estimate BOD­5 saves time and enables the adoption of an online control system. This study investigates the application of artificial neural networks (ANNs) in predicting the influent BOD5 concentration and the performance of WWTPs. The WWTP performance was defined in terms of the COD, BOD, and TSS concentrations in the effluent. Sensitivity analysis was performed to identify the best-performing ANN network structure and configuration. The results showed that the ANN model developed to predict the BOD concentration performed the best among the three outputs. The top-performing ANN models yielded R2 values of 0.752, 0.612, and 0.631 for the prediction of the BOD, COD, and TSS concentrations, respectively. The optimal performing models were obtained (three inputs – one output), which indicated that the influent temperature and conductivity greatly affect the WWTP performance as inputs in all models. The developed prediction model for the influent BOD5 concentration attained a high accuracy, i.e., R2 = 0.754, which implies that the model is viable as a soft sensor for online control and management systems for WWTPs. Overall, the ANN model provides a simple approach for the prediction of the complex processes of WWTPs.

This study examines the influence of planting mixture variations on the quality of the percolated water of the rain garden with and without plants. Six planting mixtures in experimental rain gardens have been used. It has been noted that pollutant removal efficiency of RG can exhibit variations based on specific parameters. Notably, RG6, utilizing a planting mix of 75% topsoil and 25% compost, demonstrated the highest performance. These results draw attention to the critical role of the specific planting mixtures in influencing the performance of vital parameters related to pollutant removal. The observation shows that RG5 exhibits exceptional removal efficiency in pH, Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD), and RG6 performs best in electrical conductivity (EC), Total Dissolved Solids (TDS), Total Nitrogen (TN), and Total Phosphorus (TP) removal. In particular, when analyzing pollutant removal on a surface with Madagascar periwinkle plants, RG6 emerges as the most effective, achieving an impressive efficiency of approximately 49%. For the bare surface, pollutant removal efficiency is 40%. The study outcome will be useful in deciding the composition of the planting mixture, which will keep the rain garden to improve quality and quantitatively hydrological performance, lowering urban flooding magnitude.

Landfill leachates from municipal landfills are usually heavily contaminated and thus require treatments before direct discharge into natural waters. Selecting the appropriate technology for leachate treatment is still a major challenge for operations in municipal landfills. Biodegradation is effective for treating young leachates, whereas old leachates require processes such as chemical oxidation, coagulation–flocculation, chemical precipitation, ozonation, activated carbon adsorption, and reverse osmosis. Recently, the combination of biological pretreatments followed by physico-chemical processes has been shown to be very efficient. Here we review the efficiency of biological treatment in combination with reverse osmosis to clean landfill leachates. We studied in particular processes including a membrane bioreactor, activated sludge, a rotating biological contactor, and up-flow anaerobic sludge blanket treatments, followed by reverse osmosis. We found a 99–99.5% removal of the chemical oxygen demand (COD), and a 99–99.8% removal of N–NH4+ using reverse osmosis and activated sludge. Using reverse osmosis with a rotating biological contactor, we observed 99% removal of COD, biochemical oxygen demand and N–NH4+. The combination of reverse osmosis, activated sludge and rotating biological contactor removed 98–99.2% of Cl− and 99–99.7% of Pb. Total suspended solids are best removed, up to 99%, by either a combination of reverse osmosis with membrane bioreactor, or reverse osmosis with activated sludge.

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO 3 -N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the “fair” status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land ( r  = 0.68, P  < 0.01) and a significant negative association with the built-up area ( r  =  − 0.94, P  < 0.01). To the best of the authors’ knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in Netica software and tested and verified using data covering the period 2013–2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples were used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.