Babak Zolghadr-Asli

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• Ph.D. student
• PhD Student at The University of Queensland

A Handbook on Multi-Attribute Decision-Making Methods describes multi-attribute decision-making (MADM) methods and provides step-by-step guidelines for applying them. The authors describe the most important MADM methods and provide an assessment of their performance in solving problems across disciplines. After offering an overview of decision-maki...

This book provides a comprehensive yet fresh perspective for the cutting-edge CI-oriented approaches in water resources planning and management. The book takes a deep dive into topics like meta-heuristic evolutionary optimization algorithms (e.g., GA, PSA, etc.), data mining techniques (e.g., SVM, ANN, etc.), probabilistic and Bayesian-oriented fra...

The long pursuit to alleviate the global water crisis has been riddled with revolutionary decision-making paradigms, forward-thinking theoretical concepts, and even ground-breaking technologies. This journey, however, is centered around the expectation of discovering what could be seen as the ultimate solution to all water-related problems. These n...

Over the years, desalination has become integral to water resources management, primarily in coastal semi-arid to arid regions. While desalinated seawater has mainly been supplied to municipal and high-revenue industries, the agriculture sector faces increasing irrigation demands, making it a potential user. This review assesses the sustainability...

Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming. These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effe...

The high price of desalination is often considered one of the primary obstacles to making desalinated water a viable option for irrigated agriculture. Relatively little attention has been given to how strategic planning of regional water supplies might contribute to addressing this issue, particularly in leveraging investment in desalination by wea...

Computational intelligence (CI)-based methods offer a practical approach to overcoming the significant challenges posed by analytical and enumeration optimization methods when dealing with complex real-world problems. However, a notable drawback of these algorithms is the need for time-consuming and computationally demanding fine-tuning procedures...

Linke to the article: https://theconversation.com/why-saline-lakes-are-the-canary-in-the-coalmine-for-the-worlds-water-resources-232477

The concept of computational intelligence (CI)-based optimization algorithms emerged in the early 1960s as a more practical approach to the contemporary derivate-based approaches. This paved the way for many modern algorithms to arise with an unprecedented growth rate in recent years, each claiming to have a novel and present a profound breakthroug...

Over the years, data-driven models have gained notable traction in water and environmental engineering. The adoption of these cutting-edge frameworks is still in progress in the grand scheme of things, yet for the most part, such attempts have been centered around the models themselves, and their internal computational architecture, that is, the mo...

For centuries, desalination, in one way or another, has helped alleviate water scarcity. Over time, desalination has gone through an evolutionary process influenced largely by available contemporary technology. This improvement, for the most part, was reflected in the energy efficiency and, in turn, in terms of the cost-effectiveness of this practi...

Before we can embark upon this journey of ours to learn about computational intelligence-based optimization methods, we must first establish a common language to see what an optimization problem actually is. In this chapter, we tend to take a deep dive into the world of optimization to understand the fundamental components that are used in the stru...

With all its nuances, novelties, and abstract computational structure, the harmony search algorithm is arguably one of the most formidable meta-heretic algorithms to handle discrete complex real-world optimization problems. In this chapter, we will dig deep and explore the mechanisms used in this algorithm. We would get familiar with the harmony se...

Inspired by an idealized interpretation of the echolocation characteristics of microbats, the bat algorithm holds itself as a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when it comes to handling real-world complex optimization problems. In this chapter, we will dig deep and explore the mech...

The tabu search algorithm is arguably one of the earliest attempts of computational intelligence-based optimization methods to utilize a memory-based feature to enhance the capabilities of its local search engine. Even though the computational architecture of this algorithm is not particularly complex, thanks to this very feature, the tabu search a...

Inspired by the parasitism reproduction strategy of some cuckoo bird species, the cuckoo search algorithm holds itself as a practical yet straightforward swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when it comes to handling real-world complex optimization problems. The cuckoo search algorithm is known...

The ant colony optimization algorithm is arguably the first notable attempt to introduce swarm intelligence to the field of meta-heuristic optimization. Nowadays, looking up the idea of swarm intelligence as a source of inspiration has become a topical approach to theorizing novel meta-heuristic optimization algorithms. The ant colony optimization...

Inspired by generic biological interactions found in nature, the symbiotic organisms search algorithm is a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when handling real-world complex optimization problems. The symbiotic organisms search algorithm is known to have very few parameters, which...

Inspired by an idealized representation of how a group of individuals would teach and learn a given subject, the teaching-learning-based optimization algorithm is considered one of the most notable cases of meta-heuristic algorithms that actually uses social structures and human interaction as a source of inspiration to create a search engine for a...

With all its nuances, novelties, and abstract computational structure, the differential evolution algorithm takes the idea of evolutionary computation to the next level. As such, the differential evolution algorithm is arguably one of the most formidable meta-heuristic algorithms to handle complex real-world problems. In this chapter, we will dig d...

The pattern search algorithm is arguably one of the earliest, if not the first, meta-heuristic optimization algorithms. The ideas and novelties used in this algorithm would help shape the next generation of optimization algorithms. In this chapter, we will dig deep and explore the mechanisms used in this algorithm. We would then see how one can imp...

Inspired by the biographic principle governing species’ distribution patterns in an ecosystem, the biogeography-based optimization algorithm arguably presents one of the most interesting computational structures of meta-heuristic algorithms. One of the most intriguing features of this algorithm is that it finds an elegant way to bridge the gap betw...

The genetic algorithm is arguably one of the most well-known and prevalent meta-heuristic optimization algorithms. The novelties in this algorithm created a tectonic shift in how these algorithms were generally perceived, to the point that most of these ideas were implemented in shaping the next generation of meta-heuristic algorithms. In this chap...

Inspired by the Earth’s hydrological cycle, the water cycle algorithm is a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when handling real-world complex optimization problems. In this chapter, we will dig deep and explore the mechanisms used in this algorithm. We would get familiar with the w...

Inspired by the life cycle of plants that use propagation to colonize a new habitat, the plant propagation algorithm established itself as a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when it comes to handling real-world complex optimization problems. In this chapter, we will dig deep and e...

Though it has been quite some time since the introduction of the simulated annealing algorithm, it is still considered a viable and relevant meta-heuristic optimization method. The main idea of this algorithm is to mimic the annealing process to form a search strategy that leads to identifying the optimum solution in a search space. In this chapter...

Inspired by an idealized interpretation of Newton’s law of universal gravitation, the gravitational search algorithm is a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when handling real-world complex optimization problems. In this chapter, we will dig deep and explore the mechanisms used in t...

Inspired by the life cycle of weeds as they spread through a new environment, the invasive weed optimization algorithm is arguably one of the most interesting meta-heuristic algorithms to handle complex real-world problems. What is interesting about this algorithm from a computation standpoint is that it provides a new take on how swarm intelligenc...

Inspired by the life cycle of plants that use pollination as a reproduction mechanism, the flower pollination algorithm established itself as a formidable swarm intelligence-based meta-heuristic optimization algorithm. A fairly straightforward computational structure and, more importantly, few parameters make it rather easy to implement and, in tur...

Inspired by how the memetic paradigm would influence the foraging behavior of a group of frogs as they attempt to search for a food source in their natural habitat, the shuffled frog-leaping algorithm is arguably one of the most interesting meta-heuristic algorithms to handle complex real-world problems. In this chapter, we will dig deep and explor...

The particle swarm optimization algorithm is arguably one of the most revered meta-heretic algorithms. Its abstract and straightforward computational structure allowed it to take parallelized computation to the next level. Though it was not the first swarm intelligence-based meta-heuristic algorithm per se, it absolutely played a critical role in m...

Inspired by the mating ritual of fireflies emitting bright flashing lights to attract their partners, the firefly algorithm holds itself as a formidable swarm intelligence-based meta-heuristic optimization algorithm that can be quite efficient when it comes to handling real-world complex optimization problems. In this chapter, we will dig deep and...

Maintaining access to a sustainable water resource is becoming increasingly difficult in the midst of the ongoing global water crisis, emphasizing the importance of investing in alternative resources such as desalinated water. Throughout history, the desalination industry has adapted to the specific needs of an era or different environmental condit...

Addressing the issue of shrinking saline lakes around the globe has turned into one of the most pressing issues for sustainable water resource management. While it has been established that natural climate variability, human interference, climate change, or a combination of these factors can lead to the depletion of saline lakes, it is crucial to i...

Multi-Criteria Decision-Making (MCDM) includes methods and tools for modeling and solving complex problems. MCDM has become popular in the production and service sectors to improve the quality of service, reduce costs, and make people more prosperous. This book illustrates applications through case studies focused on disaster management. With a pr...

This paper presents a multipurpose optimization algorithm (MOA) to optimize crop patterns under climate change, minimizing water use and maximizing crop revenue while enforcing food security and regional water security constraints. An application of the MOA yields a total of 12 Pareto fronts for 20-year horizons centered on 2030, 2050, 2070, and 20...

One way to choose the best option among several possible options when making a decision is to visualize the possible options. However, because each decision is made up of smaller decisions, decision-making can be a hierarchical and challenging process that may impact the decision maker’s ability to make the best decision in the shortest time. The D...

Machine learning has a special place in data science among researchers and scientists nowadays. Machine learning contains algorithms and models which permit computer systems to explore patterns in data. It is quite challenging and difficult for traditional machine learning techniques to obtain information and pattern from big and complex data. As a...

Over the years, complex problems have arisen in different science and engineering disciplines and have led to the need for computational approaches to solve these problems. Over recent decades, computational approaches, combining computer science, biology, and physiology, have given rise to a new field of science known as Artificial Intelligence (A...

FA is one of the algorithms based on collective intelligence. The basis of this algorithm is inspired by the behavior of fireflies in nature. Luminous fireflies behave in such a way that they emit the energy stored in them in the form of light to mate, hunt, flee annoying insects and protect themselves. So it can be said that light fireflies produc...

Optimization problems can be observed in many applications, ranging from engineering applications and decision-making to computer science and finance. Optimization can be described as a process for discovering optimal solution among all available solutions of the defined problem, considering complex and high-dimensional constraints in searching for...

Another supervised learning method is the support vector machine (SVM) method. This method, like the artificial neural network method, is one of the basic data methods that can classify or predict data after the training process. This theory was later used as a powerful tool for classifying data in various sciences especially in water and environme...

Optimization refers to obtaining one or more feasible solutions for a specific problem. Many real-world problems contain several competing objectives and constraints that are challenging to solve without the aid of powerful tools such as meta-heuristic optimization algorithms. In a defined multi-objective problem that requires to be optimized, ther...

One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches i...

Real-world problems often contain complex structures and various variables, and classical optimization techniques may face difficulties finding optimal solutions. Hence, it is essential to develop efficient and robust techniques to solve these problems. Computational intelligence (CI) optimization methods, such as swarm intelligence (SI) and evolut...

Lake Urmia, the twentieth largest lake in the world, is the most valuable aquatic ecosystem in Iran. The lake water level has decreased in recent years due to human activities and climate change. Several studies have highlighted the significant roles of climatic and anthropogenic factors on the shrinkage of the lake. Management policies for water r...

There is substantial evidence suggesting climate change is having an adverse impact on the world’s water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of...

From the perspective of the water–energy–food (WEF) security nexus, sustainable water-related infrastructure may hinge on multi-dimensional decision-making, which is subject to some level of uncertainties imposed by internal or external sources such as climate change. It is important to note that the impact of this phenomenon is not solely limited...

Multi-attribute decision-making (MADM) is an umbrella term to describe a series of mathematical frameworks that, in essence, enable the decision-makers to evaluate the desirability of any feasible solution by considering a set of, often, conflicting evaluation criteria. Given their vast capabilities, the MADM methods are especially helpful for wate...

Changing climate and human interference with natural phenomena are causing unprecedented changing patterns in hydro-climatic variables. These changes can manifest as dynamic changes of the stochastic properties of the datasets over time, which pose challenges for conventional time-series modeling. These datasets are dynamic in nature, even when tre...

When it comes to projecting the potential effects of climate change on hydro-climatic variables using time-series models, the conventional approach has been to examine correlations with exogenous variables. Establishing correlations among endogenous and exogenous variables, however, cannot guarantee that there is a cause-effect relationship among t...

Water resources planning and management is marked by competing interests and different values of multiple stakeholders. Thus, when it comes to making any decision with regard to water resources, different - and often conflicting - interests have to be taken into consideration. Multi-attribute decision-making (MADM) is an umbrella term to describe m...

In March 2019, the Iranian meteorological organization warned of the formation of several dense precipitation systems throughout the country. This was followed by a chain of storm events that ended up with three major floods with heavy damages, including at least 78 fatalities. Reportedly, within the first 48 h of the storm, the cumulative rainfall...

This chapter is dedicated to a family of outranking‐oriented techniques, namely, preference ranking organization methods for enrichment evaluation (PROMETHEE). The chapter begins with a brief discussion over the basic principles on which these methods are founded upon, followed by a description of the mathematical framework and the stepwise procedu...

The technique for order preferences by similarity to an ideal solution (TOPSIS) method, which is based on the foundations of the reference‐dependent theory, identifies a feasible alternative with the shortest distance to the ideal alternative, and the greatest distance from the inferior alternative. This chapter presents the basic principles of thi...

The best–worst method (BWM) is a compensatory, pairwise, comparison‐oriented MADM method described in this chapter following a brief literature review.

This chapter is dedicated to one the most popular MADM methods, namely the analytic hierarchy process (AHP). This chapter first reviews two of the main characteristics of the AHP method, namely, the pairwise comparison and the hierarchical structure, which makes the AHP an ideal tool to deal with complex MADM problems. Thereafter, the mathematical...

This chapter is dedicated to the Elimination et choix traduisant la realité (ELECTRE) family. The chapter begins with brief review of the history of this outranking‐oriented family of MADM methods followed by a stepwise description of the main members of the family, namely, ELECTRE I, II, III, and IV.

The philosophical foundations and the mathematical procedures behind one of the well‐known MADM methods, namely the analytic network process (ANP), are presented in this chapter.

This chapter is dedicated to a relatively new outranking‐oriented MADM method called superiority and inferiority ranking (SIR). This chapter begins with a brief review of this method's background and applications, followed by a summary of the mathematical and logical foundations of the method, a stepwise description of SIR, and ends with a conclusi...

This chapter introduces the basic principles of the MADM procedure and presents two widely used MADM methods, namely, the weighted sum method (WSM) and the weighted product method (WPM).

Gray system theory is a mathematical approach to address the uncertainties that are caused by lack of information and/or inaccuracy in data sets. Gray relational analysis is a branch of gray system theory that is dedicated to decision‐making under the aforementioned circumstances. This chapter presents a brief literature review of the application o...

A Benchmark Example and a Comparison between Objective‐ and Subjective‐Based MADM Methods

This chapter is dedicated to a scoring‐oriented MADM method called potentially all pairwise rankings of all possible alternatives (PAPRIKA). This chapter presents a stepwise description of the PAPRIKA, its mathematical and philosophical principles, and a context of its relation to other MADM methods.

Weight Assignment Approaches

Making a decision in a conflicting environment is an inseparable aspect of most practical MADM problems. This chapter presents the basic concepts and ideologies of the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, which is a compensatory MADM method that uses the closeness to the ideal solution to resolve the conflicting natu...

Not a day goes by without everyone having to face a decision‐making problem, whether in our personal or professional lives. This chapter defines the very act of decision‐making, and what motivates humans to make decisions. This chapter also provides guidance on how to ensure sound decision‐making when multiple goals are of concern.

In theory, the emergence of the robustness concept has pushed decision-makers toward designing alternatives, such as resistance against the potential fluctuations fueled by the uncertain surrounding environment. This study promotes an objective-based multi-attributes decision-making framework that takes into account the un certainties associated wi...

In order to support sustainable forest management, it is essential to estimate the extent and change of forest cover and to evaluate the environmental and socio-economic impacts of forest dynamics. It is challenging, however, to calculate forest area on a large scale using traditional statistical survey methods. Access to satellite images make it f...

Climate change can alter the status quo of the world as we know today. Water resources may also be influenced by these plausible impacts. The common perception is that these changes may exacerbate the situation of water resources in arid and semi-arid regions, such as the Middle-East, which are experiencing mild to severe water stress due to limite...

While many would turn to renewable resources, to mitigate the adverse impacts of over-exploitation of fossil fuels, the fact of the matter is that, if the design, operation, and management of such renewable resources are not executed with great care and consideration, they may do more harm than good. In this chapter, hydropower, as one of the most...

Water development is one of the key factors required to achieve sustainable development, poverty reduction, and even equity. This laborious, yet time-consuming task, may require in-depth investment by public sectors. There is a fallacious school of thought better known to scholars as the hot hand fallacy, however, that could consequently jeopardize...

Water resources in the Middle East region are becoming scarce, while millions of people already do not have access to adequate water for drinking and sanitary purposes. Water resources depletion has become a significant problem in this region that is likely to worsen. Current research by remote sensing analysis indicates descending trend of water s...

The assessment of climate change and its impacts on hydropower generation is a complex issue. This paper evaluates the application of representative concentration pathways (RCPs, 2.6, 4.5, and 8.5) with the change factor (CF) method and the statistical downscaling method (SDSM) to generate six climatic scenarios of monthly temperature and rainfall...

The performance assessment of water resources systems is a vital step in achieving sustainable development. A complicating factor in performance assessment is the randomness of inputs to water resources systems, such as that present in reservoir inflows. This study proposes weighted vulnerability for performance assessment of water resources system...

The natural vulnerability to the climate change phenomenon due to the unique topographic and climatic conditions in the Middle East adds significance to an already important issue of evaluating the simulations of general circulation models (GCMs) in this region. To this end, this study employed time series analysis to evaluate GCMs' simulations, in...

This study's objective is to assess the potential impact of climate change on an example under-design hydropower system in the Karkheh River basin, Iran. Based on three water resources performance criteria (reliability, resiliency, and vulnerability), a novel framework was proposed to interpret and cope with the uncertainties associated with such a...

Full text is available at: https://ascelibrary.org/doi/10.1061/%28ASCE%29WR.1943-5452.0000872

Full text is available at: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29EE.1943-7870.0001264

The dragonfly algorithm (DA) is a new metaheuristic optimization algorithm, which is based on simulating the swarming behavior of dragonfly individuals. This algorithm was developed by Mirjalili (2016) and the preliminary studies illustrated its potential in solving numerous benchmark optimization problems and complex computational fluid dynamics (...

In this chapter, some general knowledge relative to the realm of nature-inspired optimization algorithms (NIOA) is introduced. The desirable merits of these intelligent algorithms and their initial successes in many fields have inspired researchers to continuously develop such revolutionary algorithms and implement them to solve various real-world...

The krill herd algorithm (KHA) is a new metaheuristic search algorithm based on simulating the herding behavior of krill individuals using a Lagrangian model. This algorithm was developed by Gandomi and Alavi (2012) and the preliminary studies illustrated its potential in solving numerous complex engineering optimization problems. In this chapter,...

The crow search algorithm (CSA) is novel metaheuristic optimization algorithm, which is based on simulating the intelligent behavior of crow flocks. This algorithm was introduced by Askarzadeh (2016) and the preliminary results illustrated its potential to solve numerous complex engineering-related optimization problems. In this chapter, the natura...

Full text is available at: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0001553

History is full of examples concerning conflicts over water, or water wars, metaphorically. This manuscript reviews water wars from the first recorded battle to modern time warfare, with the hope of finding a remedy for this social blight. To this end, a brief chronicle review of water resources conflicts over the world illustrated that, while the...

Water resources play a crucial role in almost every aspect of human endeavors. Through water governance, which is a combination of management and development procedures to secure the safety of water resources, authorities attempt to elevate their community. A well-functioning water governance must not only address the natural dimension of water res...

Full text is available at: http://ascelibrary.org/doi/abs/10.1061/(ASCE)IR.1943-4774.0001164

For generations, the concepts of union and unionism became a familiar and inseparable agenda of social and professional part of life. Yet, through time, these social activities lost their appeal. Many unions in developed countries, faced a crisis of losing members, and their failure to replace the void amplified the issue. On the other hands, there...

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Full text is available at: http://ascelibrary.org/doi/abs/10.1061/(ASCE)IR.1943-4774.0001056