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The development of technology has made it easier for engineers to design and test models that allow simulation of real-time water distribution networks with greater accuracy. However, with so many nodes and links in a network, building a model still requires some simplification. One such simplification is the ‘conservative approach’, which applies the principle of ‘lumped demand’, taking the demand only from nodes at the ends of pipes. Herein, the full effect of lumped demand on key water parameters is analysed, on a large-scale network based on as-built networks of Al Furjan and Dubai Silicon Oasis, Dubai, UAE for different conditions. Epanet and WDNetXL software are used for the analysis, and results show the impact of different levels of skeletonisation on the head and velocity values for the two models. The analysis indicates that the head changes are high for a branched network under the extreme condition of firefighting. It includes the effect of skeletonising local tanks, with changes being higher when all tanks are empty. These findings provide a critical evaluation of the performance of this method for the Middle East region and it is concluded that the considerable velocity changes observed in the models could lead to overdesign.

The increasing interest in hydrological studies in the United Arab Emirates (UAE) has resulted in the publication of several papers on hydrology and its broad use for addressing contemporaneous challenges confronting humans and the environment in the region. However, for several reasons, these efforts have remained invisible and unrecognized. This paper has reviewed the literature on hydrological research in the UAE to provide a comprehensive source of information for researchers, practitioners, policymakers, and other stakeholders. The documented studies were carefully selected, relying on a bibliometric analysis methodology of five phases to specify the boundary of the study area, adopt the primary keywords for the search, evaluate the obtained papers, exclude the non-conforming ones, and classify the final results into four distinguished topics—namely, rainfall analysis, urban growth and flood hazards, cloud seeding and changing climate, and groundwater situation and utilization. The evaluation process considered assessing the papers’ relevancy, authenticity, and coverage of the main issues of interest. In all, a total of 50 published papers were identified based on the specified criteria and reviewed. The main findings were first that the amount of rainfall over the UAE has been declining in the last decade, and this trend is expected to continue, although intensities are rising, suggesting shorter duration events. Secondly, the extensive urban growth in the country has resulted in increasing incidences of urban floods and declining groundwater recharge. Both of these are to be expected as consequences of the increased imperviousness from urbanization and the higher intensities from shorter-duration rainfall events. Thirdly, although the cloud-seeding program has proved to be successful in increasing precipitation amounts, the impact of this on flooding due to more extreme rainfall intensities and on air quality remains worrisome. Finally, groundwater analyses have shown that it is still the main freshwater resource in the country, but its long-term sustainability and quality are being threatened by the declining recharge. This calls for a national policy for groundwater management in the UAE to tackle the challenges associated with the increasing demand for water in all sectors of the economy. The study recommended addressing the gap in the hydrological literature of the UAE, specifically in the field of big meteorological data analysis, the socioeconomic impacts of urban floods, the impacts of climate change in urbanized regions, and the possibility of using alternative resources to recharge groundwater as part of sustainable water management.

The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.

The crop water stress index (CWSI) is a reliable indicator of water status in plants and has been utilized for stress monitoring, yield prediction, and irrigation scheduling. Despite this, ...

The conventional extended aeration (EA) method has been considered as an effective method of secondary wastewater treatment in various installations around the world. The membrane bioreactor (MBR) method has been promoted by membrane manufacturers over the past decade as the most efficient treatment method in the industry. Research has shown the higher quality of MBR effluent compared with that of EA, but thecomparison has seldom included the financial aspects. If the effluent quality from both methods is acceptable for a given reuse application, then a major consideration in adopting either method must be financial (i.e., the capital expenditure (CAPEX) and operating expenses (OPEX) involved in their deployment). In this study, simulation of both methods is conducted for domestic wastewater treatment plants using the biological and financial models of CAPDETWorksTM. The CAPEX and OPEX findings from the simulation were re-based to October 2019 usingglobal price indices. Hydraulic loads from 500 to 5,000 m3/d were considered for three influent strengths: weak, medium, and strong. It is found that the EA method is less costly in meeting the effluent-quality requirements of certain reuse applications, while the MBR is recommended for the extra strong-strength influent especially for units smaller than 500m3/d, due to both effluent quality and lifetime cost.

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable, hence, it is recommended to be used for similar applications.

The treatment of dairy wastewater using traditional wastewater treatment plants such as the activated sludge is not feasible as it will require high energy rate to provide the required oxygen. This paper presents experimental results of using Vermi Bio-filtration to treat wastewater from dairy industry. Samples of wastewater were collected from Prasanthi Farms located in a small village of Kollam district, India. The sample characteristics of pH, Total Dissolved Solids, Total Suspended Solids, Chemical Oxygen Demand, Oil & Grease and Dissolved Oxygen were measured before and after vermi bio-filtration treatment. The characteristics were measured at different time intervals of 12 hours (12, 24, 36, 48, and 60 hours). The percentage changes of the above parameters after 60 hours are -18, 79, 75, 67, 69 and -31% respectively. the method is inexpensive, it can capture all organic materials, capital and working expenses are low, there is no foul scent, worm biomass would be used as nourishment for the cows, fish agro-business.

Crop water stress index (CWSI) is a reliable, economic and non-destructive method of monitoring the onset of water stress for irrigation scheduling purposes. Its application, however, is limited due to the need of obtaining the baseline canopy temperatures. This study developed a self-organizing map (SOM) based model to predict the CWSI using microclimatic variables, namely air temperature, canopy temperature and relative humidity. The canopy temperature measurements were made from Indian mustard crop grown in a humid sub-tropical agro-climate during the 2017 and 2018 cropping seasons. Eight levels of irrigation treatments (I1 – I8) based on maximum allowable depletion of available soil water were considered in the study. The CWSI for treatments I2 – I7 was computed using the empirical approach based on the experimentally measured baseline canopy temperatures from treatments I1 and I8. The number of data points used was 1260 and 1350 for model training and testing, respectively. The developed SOM model was evaluated using the error indices Nash-Sutcliffe efficiency (NSE), bias error (BE), absolute error (AE), and coefficient of determination (R2). The SOM predicted CWSI presented a good agreement with the baseline computed CWSI values during model training (R2 = 0.98, NSE = 0.97, AE = 0.018, BE = 0.0004) and testing (R2 = 0.98, NSE = 0.98, AE = 0.018, BE = 0.002). Treatment specific analysis was conducted to evaluate the performance of SOM predicted CWSI for different irrigation levels. Results indicated that the presence of zero CWSI values in a significant proportion in the dataset impacted the model prediction performance at low CWSI (

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability.

In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.

This paper presents the effect of Soil Parameter on the long drive micro-tunneling process. Terzaghi’s Silo Theory and Elastic Stress Relief Formulas are used for calculating the normal stress against the pipes. A detailed study tabulated for Al Jadaf Area across Oud Metha Road in Dubai, United Arab Emirates to check the effect of soil parameter, such as depth of excavation or invert level of micro-tunnelling, depth of ground water table, SPT Value and other soil properties. The study demonstrates that long term surface settlements confirm to the design theory limit. The high ground water table, fractures within the rock and close proximity to existing creek makes it very difficult to reduce the water table level in launching/receiving Pit and have difficulties in breaking the hard ground within the Pit. Determination of the surface settlement spot level difference is done physically at site by using latest surveying instrument, as described in the field exploration programme. By determining the difference between the two spot levels on a same coordinate, the amount of settlement and its effects on surface area can be established, which can be used as a guide line for future project and authentication of the theory.

This paper investigated the long-term trends in precipitation from 16 stations located in the lower Shire catchment in Malawi over the period 1953–2010. Annual trend analysis was first considered, and in order to take into account seasonality and serial correlation, the different months of the year are considered. Trend significance was determined using the nonparametric Mann-Kendall (MK) test statistic while the determination of the trends magnitudes was achieved using Sen’s slope method. The homogeneity of trends was examined using the Van Belle and Hughes method. The results indicate that annual precipitation has increased, whereas, monthly precipitation revealed an upward trend in wet seasons (November to April) and a downward trend in dry seasons (May to October). The monthly peak trend analysis has shown upward trend in rainy months at all stations.

The aim of this paper is to highlight established hydrology teaching methods and evaluate potential teaching enhancements. The results of applying a student-centered approach to hydrology students demonstrates that it increases student's engagement with materials, critical thinking and problem solving skills. However, this approach does not improve the conceptual understanding of hydrology for all students, as some students show resistance to a student-centered approach. Furthermore, conclusions demonstrated that not all students are ready and eager to engage in this deep learning approach employed to enhance their educational experience.

Physical and biological soil stabilities (i.e. resistance and resilience) were measured on a range of arable farms across eastern Scotland under a range of management practices, with the objective of using a geographically restricted set of soils under similar land use to detect any underlying associations between soil stability, management factors and soil properties. Data were analysed using a combination of a stepwise fixed effects model selection within a linear mixed-model framework (LMM) and neural network analysis using a Kohonen self-organising map (KSOM). In general, physical and biological measures of stability were associated with both physical and biological soil properties, particularly bulk density, water retention characteristics, soil carbon and bacterial community structure. A strength of KSOM is its ability to fit more flexible models than the linear relationships of LMM. However, a weakness is that it does not have the ability of LMM to model the sampling design, which is likely to lead to overstating statistical significance. Consequently, KSOM identified more significant associations between soil properties and stability than LMM, while the latter identified significant associations at the between-farm level. The high-level land management decisions of farm type (conventional, organic, integrated), crop type or underlying soil type were not associated with stability at this regional scale, thus indicating that the effects of different management practices between farms were overridden by the soil properties on each farm. Management decisions on improving soil stability therefore need to be taken at the individual field scale.

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling.

The discharge of construction dewatering flows to the storm drainage network for disposal is a common activity in Qatar. The Dupuit empirical approach was utilized to establish various hypothetical dewatering scenarios on the basis of site classifications, which were modeled on 4 Case Study Areas of Doha’s Existing Surface Drainage Network in order to study the impact of dewatering discharge against an established baseline. The simulations were undertaken using InfoWorks Integrated Catchment Modeling (ICM) software for critical and non-critical rainfall events. The results indicated significant localized flooding in excess of the baseline conditions for scenarios exceeding 0.5 m 3 /sec flows, while individual catchments demonstrated variations and sensitivities on the basis of catchment properties and rainfall events. It is evident that dewatering discharge under unpredictable rainfall events poses various levels of risk to the city’s infrastructure, which is further exacerbated due to the massive scale of construction activity in the country and the rising ground water table in Greater Doha Area basin.

Water resources assessment activities in inadequately gauged basins are often significantly constrained due to the insufficiency or total lack of hydro-meteorological data, resulting in huge uncertainties and ineffectual performance of water management schemes. In this study, a new methodology of rainfall-runoff modelling using the powerful clustering capability of the self-organising map (SOM), unsupervised artificial neural networks, is proposed as a viable approach for harnessing the multivariate correlation between the typically long record rainfall and short record runoff in such basins. The methodology was applied to the inadequately gauged Osun basin in southwest Nigeria for the sole purpose of extending the available runoff records and, through that, reducing water resources planning uncertainty associated with the use of short runoff data records. The extended runoff records were then analysed to determine possible abstractions from the main river source at different exceedance probabilities. This study demonstrates the successful use of emerging tools to overcome practical problems in sparsely gauged basins.

Reference crop evapotranspiration (ET"o) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g. the Penman-Monteith benchmark model, are extremely non-linear and require weather input data that are not routinely monitored. In such situations, artificial intelligence (AI), neural computing techniques that are able to accurately map complex, non-linear input-output relationships offer a useful alternative. This paper has used the Kohonen Self-Organizing Map (SOM), unsupervised artificial neural networks, to develop prediction models for the ET"o. This was achieved by using the powerful clustering capability of the SOM to analyze the multi-dimensional data array comprising the estimated ET"o (based on the FAO Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the SOM-based ET"o estimates, even when forced with fewer input data variables, were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data. Further comparisons were carried out between the SOM model estimates of the ET"o and those based on the use of feed-forward back propagation supervised artificial neural networks and the results showed that the SOM estimates were superior. Finally, the SOM-based estimates were also found to be significantly superior to those estimated using established empirical ET"o methods recommended in the literature for situations where the full complement of input weather needed to drive the Penman-Monteith model are unavailable. This offers significant potential for more accurate estimation of the ET"o in data scarce regions of the world.

A major requirement for the assessment, development and sustainable use of water resources is the availability of good quality hydrological time series data of sufficiently long duration. However, it is not uncommon to find data that are riddled with gaps, characterized by questionable quality and short durations. Sometimes, the data are just not available. Such situations are most prevalent in developing countries and the consequence is a high degree of uncertainty in the assessed characteristics of water management schemes and ultimately its ineffectual performance. Thus dealing with these problems is an important exercise in hydrological analyses. This paper focuses on the multivariate infilling of gaps for rainfall and streamflow data in the Shire River basin in Malawi, using a self organizing map (SOM) approach, which is a form of unsupervised artificial neural networks. The results show that this approach can produce reliable estimates of hydro-meteorological data thus offering promise for reducing the uncertainties associated with the use of insufficient data for water resources assessment.

The aim of the study was to analyse the causes of the flooding problems being encountered in Lagos (Nigeria) and to recommend sustainable management solutions to them. Data on climate, drainage infrastructures and physical planning regulations were collected and extensively analysed. These were combined with evidence from field inspection and discussion with stakeholders, including relevant government departments, university researchers and selected residents. The investigation revealed that, contrary to popular wisdom, climate change or unusually high rainfall is not the primary cause of the flooding problems in Lagos. Rather, the increased urbanisation, lax planning laws in relation to the erection of buildings in flood plains and the lack or inadequacy of storm drainage facilities in the city are to blame. It is argued that a lasting solution to the flooding problem will require the incorporation of sustainable drainage systems within the existing flood management strategy for the city and planning for this must start now.

The 5 days at 20 degrees C biochemical oxygen demand (BOD5) is an important parameter for monitoring organic pollution in water and assessing the biotreatability of wastewater. Moreover, BOD5 is used for wastewater treatment plant discharge consents and other water pollution control purposes. However, the traditional bioassay method for estimating the BOD5 involves the incubation of sample water for 5 days. It follows that BOD5 is not available for real-time decisionmaking and process control purposes. On the other hand, previous efforts to solve this problem by developing more rapid biosensors had limited success. This paper reports on the development of Kohonen self-organizing map (KSOM)-based software sensors for the rapid prediction of BOD5. The findings indicate that the KSOM-based BOD5 estimates were in good agreement with those measured using the conventional bioassay method. This offers significant potential for more timely intervention and cost savings during problem diagnosis in water and wastewater treatment processes.

This work presents a method that optimises the manifold pipe of a marine wastewater outfall under various hydraulic principles and cost considerations. Costing data from 37 international pipe supply companies providing pipes of various diameters for HDPE, steel and concrete were used to develop hybrid design equations for these three main materials used for outfall construction. These equations optimise the trade-off between pumping head and pipe size as well as the compromise between cost of material and favourable hydraulic characteristics. Various grades for each material were considered if relevant to marine outfalls. The resulting equations are very useful for outfall design, as oversizing of a project can incur unnecessary cost and under-sizing will not fulfil an outfall’s intended hydraulic purpose and will increase the operational cost in terms of energy

Modeling the activated sludge wastewater treatment plant plays an important role in improving its performance. However, there are many limitations of the available data for model identification, calibration, and verification, such as the presence of missing values and outliers. Because available data are generally short, these gaps and outliers in data cannot be discarded but must be replaced by more reasonable estimates. The aim of this study is to use the Kohonen self-organizing map (KSOM), unsupervised neural networks, to predict the missing values and replace outliers in time series data for an activated sludge wastewater treatment plant in Edinburgh, U.K. The method is simple, computationally efficient and highly accurate. The results demonstrated that the KSOM is an excellent tool for replacing outliers and missing values from a high-dimensional data set. A comparison of the KSOM with multiple regression analysis and back-propagation artificial neural networks showed that the KSOM is superior in performance to either of the two latter approaches.

This paper presents the challenges faced during micro-tunnelling process in Emirates of Dubai. In permeable grounds, the loss of slurry increases the cutter head torque and reduces penetration rates. Overcut is formed to provide space for converging ground and enables the evenly circulation of lubricant flow on the pipe line, as such buoyancy effects of fluid lubrication act on the pipe to lessen the drag during jacking. Micro-tunnelling techniques are used to reduce penetration resistance within the pipe string by providing sufficient uniform overcut and continuously injecting polymer lubricants. It is also observed that as the Urban area in Dubai is surrounded by built structures and numerous underground services, a special consideration is required to protect these services especially DEWA ED Cables. Also, the high ground water table, fractures within the rock and proximity of creek makes things very difficult to reduce the water table level in launching / receiving Pit and have difficulties in breaking the hard ground within the Pit. At Wafi Interchange, proposed development for the extension of interchange had to be incorporated in the alignment and accordingly standard pipe fittings to be introduced in the alignment. There are services towards Sheikh Butti palace (launching pit side) and on the other side (receiving pit). Ongoing construction of Al Jaliliya children’s hospital requires close coordination and safety related issues to be addressed prior to mobilization of heavy equipment at the proposed micro-tunnelling area. The receiving pit at Emarat petrol pump was also critical and special approval from RTA is required to close one lane at the heavy traffic along the service road, parallel to sheikh Rashid road. The removal of ground water was another challenge because near Sheikh Butti palace the drainage network was not completed and hence special arrangement was to be made to discharge the water near the creek, for which DM environment approval are to be sought.

[1] Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman-Monteith benchmark model, are time-consuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self-organizing map (KSOM), unsupervised artificial neural networks, to predict the ETo. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ETo (based on the Food and Agricultural Organization (FAO) Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM-based ETo estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM-based estimates were also found to be significantly superior to those estimated using currently recommended empirical ETo methods for data scarce situations such as those in developing countries.