Data Mining Practical Machine Learning Tools and Techniques

Transportation research. part c, emerging technologies

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.

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A review on travel behaviour modelling in dynamic traffic simulation models for evacuations

A review of water quality index models and their use for assessing surface water quality

Emergency evacuation planning is essential in disaster management. Numerous evacuation model and optimization algorithms have been developed and implemented for various types of emergencies mainly in natural and man-made disaster. This paper reviews such approaches focusing on macroscopic in emergency evacuation planning. The strength and weaknesses of algorithms and models are described. Variables considered in algorithms such as number of evacuee and evacuation time have been seen important to be applied to real evacuation scenario. Most of optimization algorithms demonstrate promising results in minimizing travelling time from the affected area to safety. Thus, algorithms should be enhanced to consider different scales of evacuation in different type of disasters. With the provision of efficient evacuation planning, it would help relevant authorities to reduce risk disaster management by offering a tool to support evacuation processes.

Optimization approaches for macroscopic emergency evacuation planning: A survey

Fuzzy theory is a powerful tool with the capability of solving many complex problems which include river water quality assessment when dealing with uncertainties data and vagueness that occur in a river system. In this paper, various fuzzy techniques are applied in the development of river water quality management; particularly, water quality index assessment is outlined. Uncertainties involved in river water quality management, especially from random nature of hydrologic variables and missing data, can be overcome using fuzzy techniques. Fuzzy interference system (FIS) plays an important role in fuzzy excursion. FIS can be improved using other methods such as similarity measures and grey clustering method to obtain better and accurate water quality assessment results. FIS can also be integrated with an expert system and a decision support system to assist the decision-maker in improving river water quality through effective strategies. Thus, the capability of FIS to be incorporated with other tec...

Development of river water quality management using fuzzy techniques: a review

The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area.

https://iopscience.iop.org/article/10.1088/1755-1315/140/1/012035/pdf

Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization

This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP) The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position A min-max approach is based on minimum capacity and maximum capacity of vehicles We analyzed the performance of the algorithms using evacuation datasets The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.

Solving vehicle assignment problem using evolutionary computation

Traffic congestion is a global and crucial problem due to increase in demand and limited network transportation structure. Complex traffic structure, imbalanced traffic flow, and uncertain event like road accident are among of the identified factors that may cause traffic congestion. Traffic congestion problem causes delay in travelling, increase in travelling cost and accident rate and may affect air pollution. Numerous approaches have been developed and proposed in solving these problems. Among them are traffic signal controls, congestion pricing, turning restrictions and traffic routing. Various methods have been employed include mathematical model, heuristic and meta-heuristics. This paper presents a review on recent approaches that have been proposed for traffic congestion problem and methods used for implementation. This review provides a bigger picture on recent approaches; provide a comparison and suggestion for future research in traffic congestion relief.

Marina Yusoff

Papers

Optimization approaches for macroscopic emergency evacuation planning: A survey

Development of river water quality management using fuzzy techniques: a review

Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization

Solving vehicle assignment problem using evolutionary computation

A Review on Recent Traffic Congestion Relief Approaches