Machine Learning Methods for Better Water Quality Prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9). © 201

Optimised neural network model for river nitrogen prediction utilizing a new training approach

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for ‘blue baby syndrome’ when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92

Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam

A novel Master–Slave optimization algorithm for generating an optimal release policy in case of reservoir operation

Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master–slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master–slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand. © 2019 Elsevier B.V

Energy analysis using carbon and metallic oxides-based nanomaterials inside a solar collector

The effectiveness of a flat-plate solar collector was studied by using SiO2, Al2O3, Graphene, and graphene nanoplatelets nanofluids with distilled water as the working fluids. The energy efficiency was theoretically compared using MATLAB programming. The prepared carbon and metallic oxides nanomaterials were structurally and morphologically characterized via field emission scanning electron microscope. The study was conducted under different operating conditions such as different volume fractions (0.25%, 0.5%, 0.75% and 1%), fluid mass flow rate (0.0085, 0.017, and 0.0255 kg/s), input temperatures (30, 40, and 50 °C), and solar irradiance (500, 750, and 1000 W/m2). Nanofluids showed better thermophysical properties compared to standard working fluids. With the addition of the nanofluids SiO2, Al2O3, Gr and GNPs to the FPSC the highest efficiency of 64.45%, 67.03%, 72.45%, and 76.56% respectively was reached. The results suggested that nanofluids made from carbon nanostructures and metallic oxides can be used in solar collectors to increase the parameters of heat absorbed/loss compared to water only usage.Validerad;2020;Nivå 2;2020-05-27 (johcin)</p

Study of Behaviour of Short Concrete Columns Confined with PVC Tube under Uniaxial Load

An experimental investigation has been carried out to evaluate the effectiveness of Polyvinyl chloride (PVC) confinements in short plain circular concrete columns. The experimental part is conducted using different PVC tube diameters (110, 160, 220, and 250 mm) with two types of confinement strategies (fully and confined with the cut ends). The results are validated with unconfined samples (control samples). The test results showed that using external confinement of concrete columns by PVC tubes enhances the ultimate load capacity of the short columns. For fully confined samples, the enhancement ratio ranges between 5% and 8.3%, and from 4.16% to 15% for samples with cut ends. Furthermore, the confining of PVC pipes with the cut ends (CCC) has a more considerable effect on load capacity for all diameters except the ones with 250 mm, where the samples with full confinement (Cc) carried a bigger load than those with cutting ends. Finally, a numerical simulation of samples is carried out by the finite element (FE) model using the ABAQUS software. For all scenarios, the results of the numerical analysis showed considerable similarity to the experimental results, with R2 of 0.95 indicating the high linearity between the actual and simulated compressive strength values. Moreover, the FE induces fewer simulated errors with a relative error (RE) ranging from 0.16% to 6% for all scenarios

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Abstract This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10–4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater

Changes in Climatic Water Availability and Crop Water Demand for Iraq Region

Decreases in climatic water availability (CWA) and increases in crop water demand (CWD) in the background of climate change are a major concern in arid regions because of less water availability and higher irrigation requirements for crop production. Assessment of the spatiotemporal changes in CWA and CWD is important for the adaptation of irrigated agriculture to climate change for such regions. The recent changes in CWA and CWD during growing seasons of major crops have been assessed for Iraq where rapid changes in climate have been noticed in recent decades. Gridded precipitation of the global precipitation climatology center (GPCC) and gridded temperature of the climate research unit (CRU) having a spatial resolution of 0.5°, were used for the estimation of CWA and CWD using simple water balance equations. The Mann–Kendall (MK) test and one of its modified versions which can consider long-term persistence in time series, were used to estimate trends in CWA for the period 1961–2013. In addition, the changes in CWD between early (1961–1990) and late (1984–2013) periods were evaluated using the Wilcoxon rank test. The results revealed a deficit in water in all the seasons in most of the country while a surplus in the northern highlands in all the seasons except summer was observed. A significant reduction in the annual amount of CWA at a rate of −1 to −13 mm/year was observed at 0.5 level of significance in most of Iraq except in the north. Decreasing trends in CWA in spring (−0.4 to −1.8 mm/year), summer (−5.0 to −11 mm/year) and autumn (0.3 to −0.6 mm/year), and almost no change in winter was observed. The CWA during the growing season of summer crop (millet and sorghum) was found to decrease significantly in most of Iraq except in the north. The comparison of CWD revealed an increase in agricultural water needs in the late period (1984–2013) compared to the early period (1961–1990) by 1.0–8.0, 1.0–14, 15–30, 14–27 and 0.0–10 mm for wheat, barley, millet, sorghum and potato, respectively. The highest increase in CWD was found in April, October, June, June and April for wheat, barley, millet, sorghum and potato, respectively.Validerad;2020;Nivå 2;2020-04-28 (johcin)</p

Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art

Developing an accurate model for discharge estimation techniques of the ungauged river basin is a crucial challenge in water resource management especially in under-development regions. This article is a thorough review of the historical improvement stages of this topic to understand previous challenges that faced researchers, the shortfalls of methods and techniques, how researchers prevailed and what deficiencies still require solutions. This revision focuses on data-driven approaches and GIS-based methods that have improved the accuracy of estimation of hydrological variables, considering their advantages and disadvantages. Past studies used artificial intelligence and geo-statistical methods to forecast the runoff at ungauged river basins, and mapping the spatial distribution has been considered in this study. A recommendation for future research on the potential of a hybrid model utilizing both approaches is proposed and described