scholarly journals The Low-Impact Development Demand Index: A New Approach to Identifying Locations for LID

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2341 ◽  
Author(s):  
Sarah Kaykhosravi ◽  
Karen Abogadil ◽  
Usman T. Khan ◽  
Mojgan A. Jadidi
Keyword(s):  
Decision Making ◽  
Low Impact Development ◽  
Environmental Index ◽  
Indirect Benefits ◽  
Hierarchical Decision ◽  
Spatial Decision Making ◽  
Urban Stormwater Runoff ◽  
Simple Additive Weighting ◽  
Decision Making Model ◽  
Socioeconomic Benefits

The primary goal of low impact development (LID) is to capture urban stormwater runoff; however, multiple indirect benefits (environmental and socioeconomic benefits) also exist (e.g., improvements to human health and decreased air pollution). Identifying sites with the highest demand or need for LID ensures the maximization of all benefits. This is a spatial decision-making problem that has not been widely addressed in the literature and was the focus of this research. Previous research has focused on finding feasible sites for installing LID, whilst only considering insufficient criteria which represent the benefits of LID (either neglecting the hydrological and hydraulic benefits or indirect benefits). This research considered the hydrological and hydraulic, environmental, and socioeconomic benefits of LID to identify sites with the highest demand for LID. Specifically, a geospatial framework was proposed that uses publicly available data, hydrological-hydraulic principles, and a simple additive weighting (SAW) method within a hierarchical decision-making model. Three indices were developed to determine the LID demand: (1) hydrological-hydraulic index (HHI), (2) socioeconomic index (SEI), and (3) environmental index (ENI). The HHI was developed based on a heuristic model using hydrological-hydraulic principles and validated against the results of a physical model, the Hydrologic Engineering Center-Hydrologic Modeling System model (HEC-HMS). The other two indices were generated using the SAW hierarchical model and then incorporated into the HHI index to generate the LID demand index (LIDDI). The framework was applied to the City of Toronto, yielding results that are validated against historical flooding records.

Presenting hydrological and data driven flood simulation-prediction methods to develop a decision-making model

2020 ◽  
Author(s):  
Mohammad Zare ◽  
Guy Schumann ◽  
Felix Norman Teferle ◽  
Patrick Matgen ◽  
Paul D. Bates
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Land Use ◽  
Flash Floods ◽  
Flood Protection ◽  
Data Driven ◽  
Flood Simulation ◽  
Protection Measures ◽  
Spatial Decision Making ◽  
Decision Making Model

<p>Flooding is the number one natural disaster in terms of insured and uninsured losses annually. The development of reliable methods for flood simulation have greatly improved our ability to predict floods thereby reducing damages and loss of life in flood-prone regions. However, there is still a lot of room for improvement and innovation to provide better predictions, especially for flash floods, particularly in urban areas  This is addressed in the present study, the goal of which it is to improve simulation and prediction of flash floods and to develop a spatial decision-making model for implementing flood protection measures. In this regard, different approaches for flood simulation and flood protection should be applied. The proposed methodology links flood hazard modeling, remote sensing and machine learning methods. Combining these physical models and data driven methods will result in a more reliable hybrid model that can be employed for prediction of (flash) floods and event analysis. In order to achieve the research goal of present study we: i) add more functionality to a hydrodynamic model code; ii) complement the latter with data driven methods ;iii) develop a spatial decision-making model framework for defining flood protection measures, iv) validate process-based and data driven methods, and finally v) cross-evaluate Light Detection And Radar (LiDAR) topography with available local super-resolution drone data to assess the ability to incorporate local flood defenses into the models. The most important outcome is the creation of valuable flood maps in areas where it matters - while accounting for effects of land use and climate change. This will serve scientists as well as land and risk management authorities with better actionable flood risk information in locations where people and assets are located and in danger. It also develops innovative methodologies for estimating the changing risk from flash floods based on land use scenarios and climate change projections. Moreover, developing spatial multi-criteria decision making (SMCDM) can help decision makers to determine suitable locations and methods for flood protection measures. These methods will be particularly valuable in the context of solving current challenges of accounting for and mitigating flash floods and the effects of climate change.</p>

scholarly journals Monkey Plays Pac-Man with Compositional Strategies and Hierarchical Decision-making

2021 ◽  
Author(s):  
Qianli Yang ◽  
Zhongqiao Lin ◽  
Wenyi Zhang ◽  
Jianshu Li ◽  
Xiyuan Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Video Game ◽  
Hierarchical Decision Making ◽  
New Approach ◽  
Advanced Level ◽  
Macaque Monkeys ◽  
Take The Best ◽  
Heuristic Strategies ◽  
Hierarchical Decision ◽  
Decision Making Model

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals' advanced cognition.

Three level hierarchical decision making model with GA

2014 ◽  
Vol 31 (6) ◽  
pp. 1116-1128 ◽  
Author(s):  
Egidio D’Amato ◽  
Elia Daniele ◽  
Lina Mallozzi ◽  
Giovanni Petrone
Keyword(s):  
Decision Making ◽  
Hierarchical Decision Making ◽  
Hierarchical Decision ◽  
Decision Making Model

The Research of Centralized Robot-Soccer Decision-Making System Based on Three-Layered Hierarchical Model

2014 ◽  
Vol 543-547 ◽  
pp. 1327-1330 ◽  
Author(s):  
Wei Zhao ◽  
Wen Juan Huang ◽  
Hong Ping Yao ◽  
Jian Yu Gao
Keyword(s):  
Decision Making ◽  
Hierarchical Model ◽  
Organizational Model ◽  
Soccer Robot ◽  
Hierarchical Decision Making ◽  
Robot Soccer ◽  
Hierarchical Decision ◽  
Decision Making System ◽  
Decision Making Model

This paper describes robot soccer, it studies the organizational model of the soccer robot, and centralized decision-making system is analyzed. With the deep research of the three-layered hierarchical decision-making model on the basis of decision-making, management and executive, the paper achieves the basic control of the robot.

scholarly journals KOMPARASI METODE ANALYTICAL HIERARCHY PROCESS (AHP) DAN SIMPLE ADDITIVE WEIGHTING (SAW) DALAM PENENTUAN SKALA PRIORITAS PROYEK PEKERJAAN (Studi Kasus : CV. EUK Samarinda )

2019 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Achmad Jaya Adhi Nugraha
Keyword(s):  
Decision Making ◽  
Analytical Hierarchy Process ◽  
Project Selection ◽  
Selection Method ◽  
Ahp Method ◽  
A Priority ◽  
Simple Additive Weighting ◽  
Decision Making Model ◽  
Hierarchy Process ◽  
Better Than

Semua proyek pekerjaan yang dikeluarkan oleh PT. PLN (Persero) Wilayah Samarinda secara resmi diumumkan melalui e-procurement. Masalah yang muncul adalah bagaimana CV. EUK dapat menentukan tawaran proyek pekerjaan mana yang layak untuk dikerjakan sehingga secara tidak langsung dapat mempengaruhi kenaikan harapan pada Laba Kotor yang akan diterima. Masalah ini juga terkait langsung bagaimana menentukan penjadwalan proyek kerja yang dilakukan pada skala prioritas. Metode AHP diterapkan untuk menentukan Skala Prioritas Kerja yang akan dipilih dengan B / C Analysis sebagai model pengambilan keputusan. Diharapkan penerapan metode ini secara tidak langsung dapat meminimalkan keterlambatan dalam penyelesaian pekerjaan dan meningkatkan harapan penerimaan Laba Kotor. Sebagai pembanding digunakan metode SAW. Hasil penelitian menunjukkan bahwa metode AHP jauh lebih baik daripada metode SAW, dan mampu menunjukkan kelemahan metode pemilihan proyek yang digunakan CV. EUK sejauh ini.All job projects issued by PT. PLN (Persero) Area Samarinda is officially announced through e-procurement. The problem that arises is how the CV. EUK can determine which job project bid is feasible to work so that it can indirectly affect the increase of expectation on Gross Profit to be received. This problem is also directly related to how to determine the scheduling of work projects undertaken on a priority scale. The AHP method is applied to determine the Work Priority Scale to be selected with B / C Analysis as a decision-making model. It is hoped that the implementation of this method can indirectly minimize the delay in the time of completion of the work and increase the expectation on the acceptance of Gross Profit. As a comparison used SAW method. The results showed that AHP method is much better than SAW method, and able to show weakness of project selection method used CV. EUK so far. 

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