Application of Wavelet Decomposition and Phase Space Reconstruction in Urban Water Consumption Forecasting: Chaotic Approach (Case Study)

The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two pre-processing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multilinear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, autocorrelation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models’ relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE) and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values

Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.EF-O acknowledges the financial support provided by the “Agencia de Gestió d’Ajust Universitaris I de Recerca” (https:// agaur. gencat. cat/ en/) through the Industrial Doctorate Plan of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, under the Grant DI 093-2021. Additionally, EF-O appreciates the economic support received from the Water Utility Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l'Aigua.Peer ReviewedPostprint (published version

Forbedring av koagulant-doseringskontroll i renseprosesser for vann og avløp

Chemical coagulation is one of the most important treatment processes in wastewater treatment and drinking water treatment. Defining the optimal coagulant dosage is a vital operation that decides the treatment efficiency and economy of the coagulation process. Chemical coagulation is a well-defined process where the optimal coagulant dosage is dependent on the influent quality, expressed by particle concentration, pH, temperature, colour or phosphate, alkalinity, etc. However, no conceptual model has been developed due to the complexity of this process and the research on coagulant dosage control has continued for decades (Ratnaweera and Fettig, 2015). Among all the avenues of research, the model predictive control based on online measurements is the most promising concept for coagulant dosage control. It presents various methods of model calibration and well-defined testing procedures. A Feed-Forward (FF) model based concept of a multi-parameter dosing control system for wastewater was originally proposed by Ratnaweera et al. (1994) and then improved upon by Lu (2003) and Rathnaweera (2010). According to previous results of full-scale tests, the multi-parameter dosing control system has proven to provide acceptable effluent quality and improved economy on most occasions in several wastewater treatment plants.Kjemisk felling er en av de viktigste enhetsprosessene i både avløps- og drikkevannsbehandling. Identifisering av optimal koagulantdose er sentralt i driften av koaguleringsprosessen, og avgjørende for både rensegraden og driftsøkonomien i prosessen. Kjemisk felling er en veldefinert prosess der den optimale koagulantdosen avhenger av kvaliteten på innkommende vann, gitt ved partikkelkonsentrasjon, pH, temperatur, farge eller fosfatinnhold, alkalinitet osv. Det finnes imidlertid ingen universielle konseptuell modell for å bestemme optimal dose ettersom prosessen er svært kompleks. Dette har ført til årtier med forskning på regulering av koagulantdosen (Ratnaweera og Fettig, 2015). Av de ulike forskningsretningene har prediktiv regulering basert på online målinger vist seg svært populært, og inkluderer forskjellige metoder for modellkalibrering og definerte testprosedyrer. Et konsept bestående av multi-parameter doseringsregulering for avløpsrensing ble opprinnelig foreslått av Ratnaweera et al. (1994) og forbedret av Lu (2003) og Rathnaweera (2010). Tidligere fullskala tester har vist at systemet for multi-parameter doseringsregulering gir akseptabel kvalitet på behandlet vann og forbedret driftsøkonomi i et antall avløpsbehandlingsanlegg.DOSCON A

Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications

This 2005 version has been superseded by the 2017 edition, available in full here: http://hdl.handle.net/1813/48159Throughout history much of the world has witnessed ever-greater demands for reliable, high-quality and inexpensive water supplies for domestic consumption, agriculture and industry. In recent decades there have also been increasing demands for hydrological regimes that support healthy and diverse ecosystems, provide for water-based recreational activities, reduce if not prevent floods and droughts, and in some cases, provide for the production of hydropower and ensure water levels adequate for ship navigation. Water managers are challenged to meet these multiple and often conflicting demands. At the same time, public stakeholder interest groups have shown an increasing desire to take part in the water resources development and management decision making process. Added to all these management challenges are the uncertainties of natural water supplies and demands due to changes in our climate, changes in people's standards of living, changes in watershed land uses and changes in technology. How can managers develop, or redevelop and restore, and then manage water resources systems - systems ranging from small watersheds to those encompassing large river basins and coastal zones - in a way that meets society's changing objectives and goals? In other words, how can water resources systems become more integrated and sustainable

Assessment of Water Quality using Machine Learning and Fuzzy Techniques

The water quality of river Ganga is an important concern due to its drinking, domestic uses, irrigation and also for aquatic life. But the extent of pollutants in river water has deteriorated the quality of river water. So, the assessment of river water becomes very important. But due to the involved subjectivity and uncertainty in the decision making parameter makes the task very complex. In this study, machine learning and fuzzy techniques are utilized to develop the river water quality assessment models. The quality of the water is grouped into three classes. Four machine learning algorithms namely decision tree, random forest tree, k-nearest neighbor and support vector machine are used and implemented on python and anaconda platform. Whereas, three fuzzy based models (fuzzy decision tree, wang-mendel and fast prototyping) are developed using Guaje open source software. All the seven models are analyzed in terms of accuracy, precision, recall and f1-score. The observed result shows that the fuzzy decision tree-based assessment model performs more accurately as compared with the machine learning based models

Modeling Jar Test Results Using Gene Expression to Determine the Optimal Alum Dose in Drinking Water Treatment Plants

يعد التخثير من أهم العمليات في محطات تنقية مياه الشرب، ان استخدام مخثر الشبة يؤدي الى زيادة تركيز الألمنيوم المتبقي في المياه، والذي تم ربطه في العديد من الدراسات بمرض الزهايمر. لذلك فإنه من المهم استخدامه في الجرع الأمثل. في هذه الدراسة، تم إجراء أربع مراحل من التجارب لتحديد تأثير مواصفات المياه الخام، مثل: العكارة، pH، القلوية والحرارة على الجرعة الأمثل من مخثر الشبة  [   .14 O] للحصول على علاقة رياضية تمكن من الاستغناء عن الحاجة لتجارب الجرة. تم إجراء التجارب بظروف مختلفة وعلى مدى فصول السنة، وتم تحديد الجرعة الأمثل لكل دورة من التجارب لتشكيل نموذج يعتمد على التعابير الجينية.تم بناء النموذج عن طريق بيانات مواصفات المياه: العكارة، pH، الحرارة والقلوية للتنبؤ بقيمة جرعة الشبة الأمثل اللازمة، النموذج الذي تم الحصول عليه أعطى نتائج جيدة بمعامل ارتباط 0.91   ومربع جذر الأخطاء 1.8.  تمت مقارنة النتائج مع نموذج انحدار خطي والذي لم يكن كافيا لإعطاء نتائج جيدة نظرا لطبيعة العلاقة الغير خطية المعقدة.  سلاسل أخرى من التجارب تم القيام بها لتحديد الجرعة الأمثل اللازمة أثناء حدوث الفيضانات والتي تصل فيها قيم عكارة الى قيم عالية مع دراسة استخدام النشاء كمادة مساعدة للتخثير، تم الحصول على نموذج جيد للتنبؤ، بقيمة معامل 0.92 وجذر مربعات أخطاء   5.1.Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [   .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were constructed using data of the jar test experiments: turbidity, pH, alkalinity, and temperature, to predict the coagulant dose. The best GEP model gave very good results with a correlation coefficient (0.91) and a root mean square error of 1.8. Multi linear regression was used to be compared with the GEP results; it could not give good results due to the complex nonlinear relation of the process. Another round of experiments was done with high initial turbidity like the values that comes to the plant during floods and heavy rain. To give an equation for these extreme values, with studying the use of starch as a coagulant aid, the best GEP gave good results with a correlation coefficient of 0.92 and RMSE 5.

Modelling COD concentration by using three different ANFIS techniques

Artificial intelligence (AI) techniques have been successfully performed in many different water resources applications such as rainfall-runoff, precipitation, evaporation, discharge (Q), dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), sediment concentration and lake levels by many researchers over the last three decades. In this study, three different adaptive neuro-fuzzy inference system (ANFIS) techniques, ANFIS with fuzzy clustering (ANFIS-FCM), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), were developed to estimate COD concentration by using various combinations of daily input important variables water suspended solids (SS), discharge (Q), temperature (T) and pH. Root mean square error (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics were used for the comparison criteria. Training, testing and validation phase’s results of the optimal ANFIS models were also graphically compared each other. Comparison of the results indicated that the ANFIS-SC(1,0.3,1) model whose input is water SS was found to be slightly better than the other models in estimation of COD according to the comparison criteria in testing phase. In the validation phase, however, ANFISFCM( 1,3,gauss,1) model performed slightly better than ANFIS-GP(3,trimf,constant,1) and ANFIS-SC(1,0.3,1) models. It can be said that three different ANFIS techniques provide similar accuracy in estimating COD

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system