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This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

Ballast water of merchant ship is a source of introduction of invasive species around the globe. The purpose of this paper is to present a quantitative risk assessment applied to a model port, the Port of Arzew in Algeria, and based on an analysis of this port’s shipping traffic.

The risk assessment for introduction of invasive species is interpreted in the form of a probabilistic process, with a combination of two probabilities. The first probability is related to the ability of a species to arrive to the destination (recipient port), depending on the quantity of water ballast discharged and the duration of voyage. The second one is based on the species ability to survive in their new environment, which depends on the environmental similarity between donor port and Arzew port.

This assessment’s outcome consists on a classification of scenarios regarding their acceptability. Consequently, it helped to classify donor ports according to a risk scale, from low risk to high-risk donor ports.

The phenomenon of invasion of aquatic species is a complex process. Factors such as adaptation and tolerance of species, the attendance or absence of predators, were not taken into account in this study.

This study could be used by the maritime administration as a decision-making tool regarding the issue of exemptions under the IMO International Convention on the Management of Ballast Water and Sediments 2004.

This is one of the first known studies in Algeria and dealing with ballast water management. The results of this assessment provide useful information to policy makers, in order to develop a national strategy to reduce the impact of shipping pollution on the marine environment.

In recent years, with the increasingly serious urban water environmental pollution, the government has exerted considerable effort to promote the comprehensive improvement of the water environment and conducted numerous urban water environment treatment public–private partnership (PPP) projects (UWETP-PPP). The sustainability assessment of UWETP-PPP has important theoretical and practical significance for the public and private sectors to systematically evaluate the sustainability of projects and promote the sustainable development of projects. Therefore, the purpose of this paper is to advance a method to evaluate the sustainability of UWETP-PPP.

The paper adopted fuzzy logic method to evaluate the sustainability of UWETP-PPP. First, this paper constructed the sustainability assessment indicator system of UWETP-PPP. Second, this paper determined the appropriate linguistic scale for assessing the performance ratings and importance weights of UWETP-PPP sustainability indicators and collected experts’ evaluations of the project through questionnaires. Then, the linguistic variables were approximated as fuzzy numbers through the concept of fuzzy theory. Finally, the sustainability of the project was determined and the weak sustainability aspects of the project were identified by calculation.

The feasibility of this method was verified by a specific case. And, the results of the case study showed that the UWETP-PPP in Xuchang City, China, was sustainable, and the indicators of weak sustainability of the project were identified, namely, internal return ratio (IRR), sustainable cash flow, provision of employment opportunities, energy efficiency, comprehensive use of water resources, use of innovation materials, renewal of project facilities, adoption of advanced engineering technology, use of construction waste and waste recycling and reuse. This paper also proposed corresponding suggestions for further improving the sustainability of the UWETP-PPP in Xuchang City, China.

This paper makes two contributions: first, the fuzzy logic method is applied to provide new ideas for a sustainability assessment of UWETP-PPP, and the assessment results facilitate ways to monitor and estimate the degree of sustainability. Second, the identified indicators of weak sustainability provide useful references for the public and private sectors regarding implementing appropriate measures to improve the sustainability level of UWETP-PPP.

The purpose of this paper is to assess the ground water quality and salinity issues in the fast developing coastal urban lands of two river basins of Thiruvananthapuram district, Kerala, South India.

In order to address the water quality of the basins, field sampling was conducted and the samples were analysed in the laboratory. A comparison with water quality standards was also made and the interpretations of the results were done using GIS and statistical tools.

The values of conductivity, chlorides and salinity show that the coastal areas of Neyyar and Karamana basins are severely affected by salinity intrusion in addition to the pollution problems. More than 90 per cent of the samples are with hardness lower than 100 mg/l. About 70 per cent of the study area is with calcium concentrations lower than 25 mg/l. The content of sulphate and magnesium in Poovar and Poonthura coastal stretches is found to be higher compared to other regions.

Since not much work has been published from the study area on these aspects, the hydrochemical characterization is a very important in deciphering the quality of ground water for its proper management. The water quality evaluation and salinity intrusion studies are very important for the future planning and development of this area.

A systems perspective of waste management allows an integrated approach not only to the five basic functional elements of waste management itself (generation, reduction, collection, recycling, disposal), but to the problems arising at the interfaces with the management of energy, nature conservation, environmental protection, economic factors like unemployment and productivity, etc. This monograph separately describes present practices and the problems to be solved in each of the functional areas of waste management and at the important interfaces. Strategies for more efficient control are then proposed from a systems perspective. Systematic and objective means of solving problems become possible leading to optimal management and a positive contribution to economic development, not least through resource conservation. India is the particular context within which waste generation and management are discussed. In considering waste disposal techniques, special attention is given to sewage and radioactive wastes.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

Brazilian sediment quality criteria were established based on pseudo-total metal contents, which is not suitable to determine their bioavailability and the ecological risk for aquatic biota. Therefore, the geochemical speciation of five metals (Cu, Fe, Ni, Pb and Zn) was determined to assess the distribution and ecological risk of sediments in a tropical shallow reservoir from Brazil. The paper aims to discuss these issues.

A survey of metals in ten representative stations of surface sediments (0-20 cm) from Carlos Botelho (Lobo-Broa) Reservoir was conducted during the dry season in April 2014. Sediment parameters were measured in situ. The geochemical speciation of metals was carried out using the modified three-stage BCR-701 procedure (Davutluoglu et al., 2011) and compared with pseudo-total metal contents.

Contamination at S-1 and S-8 was more severe than other sampling sites, especially for Cu, Ni, Pb and Zn. Metals such as Cu, Ni and Zn occasionally may be associated with adverse biological effects based on the comparison with sediment quality guidelines. The risk assessment code suggests no risk for Fe and low risk for Cu and Ni.

Geochemical speciation of metals could be useful in developing effective management strategies to control metal pollution in the Lobo-Broa Reservoir. Substantial amounts of Pb (classified as medium risk) were bound to humic substances and/or insoluble metal sulfides, and could pose serious risk to the benthic community through the food chain. Zinc at S-1, S-3 and S-8 showed high risk, indicating that an appreciable portion of Zn could be available to aquatic biota.

Aim of this research work is to examine the stress–strain behavior and modulus of elasticity of fiber-reinforced concrete (FRC) exposed to elevated temperature. The purpose of this paper is to study the effect of standard fire exposure on the mechanical and microstructure characteristics of concrete specimens with different strength grade.

An electrical bogie hearth furnace was developed to simulate the ISO 834 standard fire curve. Specimens were exposed to high temperatures of 821°C, 925°C and 986°C for the duration of 30, 60 and 90 min, respectively, as per standard fire curve. Peak stress, peak strain, modulus of elasticity and damage level of heated concrete specimens were evaluated by experimental investigation. SEM-based microstructure investigation has been carried out to analyze the microstructure characteristics of heated concrete specimens.

The results revealed that carbon fiber reinforced concrete was found to be better than the FRC made with other fibers on improving the modulus of elasticity of concrete. An empirical relationship has been established to predict the modulus of elasticity of temperature exposed specimens with different type of fiber and grade of concrete. In comparison with low melting point fibers, high melting point fibers exhibited higher modulus of elasticity under all tested conditions. Surface damage and porosity level of concrete with carbon and basalt fibers were found to be lower than other FRC.

Empirical relationship was developed to determine the modulus of elasticity of concrete exposed to elevate temperature, and this will be useful for concrete design applications. This research work may be useful for finding the residual compressive strength of concrete exposed to elevate temperature. So that it will be helpful to identify the suitable repair/retrofitting technique for reinforced concrete elements.

This paper aims to present a practical application of Self Organizing Map (SOM) and decision tree algorithms to model a multi‐response machining process and to provide a set of control rules for this process.

SOM is a powerful artificial neural network approach used for analyzing and visualizing high‐dimensional data. Wire electrical discharge machining (WEDM) process is a complex and expensive machining process, in which there are a lot of factors having effects on the outputs of the process. In this work, after collecting a dataset based on a series of designed experiments, the paper applied SOM to this dataset in order to analyse the underlying relations between input and output variables as well as interactions between input variables. The results are compared with the results obtained from decision tree algorithm.

Based on the analysis of the results obtained, the paper extracted interrelationships between variables as well as a set of control rules for prediction of the process outputs. The results of the new experiments based on these rules, clearly demonstrate that the paper's predictions are valid, interesting and useful.

To the best of the authors' knowledge, this is the first time SOM and decision tree has been applied to the WEDM process successfully.