Spatial Decision Support Systems with Automated Machine Learning: A Review

Article Computer Science, Interdisciplinary Applications

Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea

Nikolay O. O. Nikitin et al.

Summary: This paper focuses on the automated solution of well optimization and reservoir characterization using automatic machine learning methods. The study introduces the application of AutoML in reducing the time required for routine tasks.

COMPUTERS & GEOSCIENCES (2022)

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Article Engineering, Civil

Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms

Yuchen Guo et al.

Summary: This study proposed the use of AutoML to construct rapid early warning and comprehensive analysis models for urban waterlogging, and compared with other three machine learning algorithms, AutoML outperformed in terms of performance and consistency with monitoring results.

JOURNAL OF HYDROLOGY (2022)

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Article Environmental Studies

Charting the past and possible futures of planning support systems: Results of a citation network analysis

Claire Daniel et al.

Summary: In recent years, there has been an increased interest in the use of digital technology in urban planning. This study conducts a citation network analysis on the literature of Planning Support Systems (PSS) to understand the overall structure of the field and its research streams. The findings reveal the characteristics of technical and applied research, and emphasize the need for a refreshed approach in this field. The analysis provides a valuable framework to guide future digital planning efforts.

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE (2022)

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Article Urban Studies

Incorporating Planning Intelligence into Deep Learning: A Planning Support Tool for Street Network Design

Zhou Fang et al.

Summary: The study introduces a novel approach leveraging deep neural networks and planning guidance to automate street network generation, suggesting that incorporating planning knowledge improves the accuracy of model predictions. The new tool allows both professionals and lay users to systematically and intuitively explore benchmark proposals for comparisons and evaluations.

JOURNAL OF URBAN TECHNOLOGY (2022)

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Article International Relations

Forecasting conflict in africa with automated machine learning systems

Vito D'Orazio et al.

Summary: The ViEWS problem aims to predict changes in state-based violence levels at the PRIO-GRID and country level for the next six months. Through utilizing automated machine learning (autoML) systems and limited datasets that focus on the endogenous nature of conflict, the study reveals two key findings: autoML enhances predictive performance and the Dynamics model yields the best results. The Dynamics model incorporates data on state-based violence derived from event-level violence data, as well as spatial and temporal characteristics. This model successfully captures conflict dynamics while avoiding overfitting to external factors. At the PGM level, the Dynamics model emerged as the winner of the ViEWS competition in terms of predictive accuracy and originality. Beyond the competition, the study suggests that conflict forecasting models combining advanced autoML systems with variables reflecting diverse conflict dynamics are likely to achieve high predictive performance, particularly at sub-national and sub-annual levels.

INTERNATIONAL INTERACTIONS (2022)

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Review Computer Science, Artificial Intelligence

AutoML: state of the art with a focus on anomaly detection, challenges, and research directions

Maroua Bahri et al.

Summary: The past decade has witnessed rapid development in machine learning research, but the performance of multiple algorithms is sensitive to factors such as hyper-parameter tuning and data cleaning, requiring significant human effort to achieve good results. Automated machine learning (autoML) aims to make machine learning algorithms more accessible and reliable, with most research efforts focusing on supervised learning and few proposals for unsupervised learning.

INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS (2022)

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Review Computer Science, Interdisciplinary Applications

Review of ML and AutoML Solutions to Forecast Time-Series Data

Ahmad Alsahref et al.

Summary: Time-series forecasting is an important discipline in data modeling that utilizes past observations to predict future values. This article reviews the methods of analyzing time series, from traditional linear modeling techniques to automated machine learning (AutoML) frameworks and deep learning models. The objective is to identify the challenges of time-series forecasting and the techniques used to address them. The article serves as a guide and reference for researchers and industries using AutoML for forecasting, while also highlighting gaps in previous works and forecasting techniques.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2022)

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Article Computer Science, Information Systems

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

Yanan Wang et al.

Summary: This article proposes a novel Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and coordinating their control.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Geography, Physical

Multi-modal spatio-temporal meteorological forecasting with deep neural network

Xinbang Zhang et al.

Summary: Meteorological forecasting is a fundamental problem in remote sensing, and long-term prediction still relies on traditional methods. To improve the forecasting ability with big meteorological data, this article proposes a deep learning framework using AutoML to model the dynamics of multi-modal meteorological data in spatial and temporal dimensions.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2022)

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Article Environmental Sciences

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

Kai-Yun Li et al.

Summary: This study integrates autonomous computation and AI technologies with a hyperspectral system to estimate crop yield and biomass. The research shows the significant estimation capacity of the AutoML regression model and highlights the economic and environmental benefits of the hyperspectral system in sustainable and intelligent agriculture.

REMOTE SENSING (2022)

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Article Ecology

A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales

Fearghal O'Donncha et al.

Summary: This study introduces a novel spatio-temporal LSTM architecture for time series forecasting in environmental datasets, demonstrating its ability to accurately replicate complex signals and provide high performance. Learning from multiple sensors simultaneously can enhance robustness to missing data.

ECOLOGICAL INFORMATICS (2022)

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Article Environmental Sciences

A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt

Shahid Nawaz Khan et al.

Summary: This study proposes a method called Geographically Weighted Random Forest Regression (GWRFR) to improve crop yield prediction. The GWRFR outperforms other machine learning algorithms and can better address the spatial non-stationarity issue. This method has the potential to be used for yield prediction of other types of crops in different regions.

REMOTE SENSING (2022)

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Article Geosciences, Multidisciplinary

Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods

Zhigang Bai et al.

Summary: In this study, four methods of machine learning, ensemble learning, deep learning, and automated machine learning (AutoML) were used for comprehensive groundwater potential mapping (GPM) in Hubei Province, China. The results showed that AutoML had the best predictive ability and accuracy compared with the other three methods. Geology and precipitation were identified as the two factors with the greatest influence on groundwater potential.

NATURAL RESOURCES RESEARCH (2022)

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Proceedings Paper Geography, Physical

AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLEBASED HYPERSPECTRAL AND LIDAR DATA FUSION

Kamila Dilmurat et al.

Summary: The combination of remote sensing data and artificial intelligence enables agriculture stakeholders to monitor changes in crops and predict pre-harvest yield accurately. In this study, UAV-based LiDAR and hyperspectral images were used to predict maize yield, and the results suggest that the combination of hyperspectral imagery and canopy structure features improves prediction accuracy. The H2O Automated Machine Learning framework is found to be an efficient strategy for machine learning-based model building.

XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III (2022)

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Article Computer Science, Theory & Methods

Human behavior in image-based Road Health Inspection Systems despite the emerging AutoML

Thitirat Siriborvornratanakul

Summary: The study confirms the trending rise of AutoML through search engine data, inspects three popular AutoML frameworks, and focuses on recent image-based machine learning research. It reveals that recent works in image-based road health inspection systems have not utilized AutoML yet.

JOURNAL OF BIG DATA (2022)

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Article Chemistry, Analytical

Predicting Grape Sugar Content under Quality Attributes Using Normalized Difference Vegetation Index Data and Automated Machine Learning

Aikaterini Kasimati et al.

Summary: This paper proposes an innovative approach for predicting grape quality attributes by combining AutoML techniques and NDVI data. The results show promising performance of UAV and Spectrosense+ GPS data in predicting grape sugars.

SENSORS (2022)

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Article Computer Science, Artificial Intelligence

AutoML: A survey of the state-of-the-art

Xin He et al.

Summary: Deep learning techniques have achieved remarkable results in various tasks, but building a high-quality DL system requires human expertise. Automated machine learning is a promising solution that is currently being extensively researched.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Environmental Sciences

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

Alexander Y. Sun et al.

Summary: The study used an automated machine learning workflow to reconstruct GRACE-like data and fill the gap between two GRACE missions. The results showed satisfactory performance in testing over the CONUS, indicating the importance of using multiple machine learning models in combination for training and optimization.

WATER RESOURCES RESEARCH (2021)

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Article Computer Science, Software Engineering

PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines

Jorge Piazentin Ono et al.

Summary: AutoML methods can generate ML pipelines, but their black-box nature and complexity make it challenging for developers and ML experts. PipelineProfiler is an interactive visualization tool that helps users explore and compare the ML pipelines produced by AutoML systems, providing a better understanding and insights for improvement.

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS (2021)

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Article Computer Science, Artificial Intelligence

Benchmark and Survey of Automated Machine Learning Frameworks

Marc-André Zöller et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2021)

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Article Environmental Sciences

Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment

Patricia Arrogante-Funes et al.

Summary: Among natural hazards, landslides pose a significant risk to human life and property, requiring risk assessment tools for effective prevention and management. Integrating susceptibility and vulnerability maps has become a key method for studying landslide-prone sites. This study presents a heuristic risk model utilizing susceptibility and vulnerability mapping, showing potential for adapting to different regions with low cost and high frequency adaptability.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

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Article Environmental Sciences

Landslide Susceptibility Assessment Using an AutoML Framework

Adrian G. Bruzon et al.

Summary: The risks associated with landslides can be effectively mitigated by new methodologies that identify and understand landslide hazards. The study developed a methodology based on an automatic machine learning framework and tested it in Guerrero (Mexico) with 16 machine learning algorithms, achieving promising results. The use of AutoML allows researchers to focus on data analysis and gain better knowledge about landslide processes.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2021)

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Article Environmental Sciences

Geographically and temporally weighted neural network for winter wheat yield prediction

Luwei Feng et al.

Summary: This study confirmed the presence of spatial and temporal non-stationarity in winter wheat yield prediction through geographically weighted regression and temporally weighted regression, and proposed a new Geographically and Temporally Weighted Neural Network (GTWNN) model.

REMOTE SENSING OF ENVIRONMENT (2021)

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Article Environmental Sciences

Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning

Ebrahim Babaeian et al.

Summary: This study aimed to develop a new approach for estimating root zone soil moisture by fusing high spatial resolution optical reflectance UAS observations with soil information on an AutoML platform. Results showed that integrating physical and hydraulic soil properties with UAS optical observations improved soil moisture estimation, especially for the root zone.

REMOTE SENSING OF ENVIRONMENT (2021)

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Article Environmental Sciences

An Automated Machine Learning Framework in Unmanned Aircraft Systems: New Insights into Agricultural Management Practices Recognition Approaches

Kai-Yun Li et al.

Summary: The integration of AutoML technology with unmanned aircraft systems (UAS) in image-based data classification tasks holds significant potential for agricultural management practices (AMP).

REMOTE SENSING (2021)

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Article Environmental Sciences

Machine Learning to Estimate Surface Roughness from Satellite Images

Abhilash Singh et al.

Summary: The study applied Support Vector Regression (SVR) to estimate surface roughness on the Kosi River alluvial fan from satellite images. Through feature processing and model comparison, a PCA-MM-SVR method showed better performance, providing a robust approach for high-resolution surface roughness measurement solely from satellite data.

REMOTE SENSING (2021)

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Article Chemistry, Multidisciplinary

AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling

Saki Gerassis et al.

Summary: Researchers are introducing an innovative AI-based approach to assess environmental risks associated with mining activities, constructing an Environmental Impact Assessment (EIA) index. The results show a moderate environmental impact of mining activities, highlighting the importance of protecting environmental factors related to surface and subsurface runoff, species, and soil degradation.

APPLIED SCIENCES-BASEL (2021)

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Article Public, Environmental & Occupational Health

Exploring the potential for planning support systems to bridge the research-translation gap between public health and urban planning

Paula Hooper et al.

Summary: This research explores the role of planning support systems (PSS) in translating and applying health evidence into urban planning and design practices to create healthy, liveable communities. By customizing existing software, a health impact PSS (the Urban Health Check) was developed and piloted, showing positive impact in visualization, understanding, communication, and improvement of health outcomes in planning and design decisions.

INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS (2021)

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Article Environmental Studies

Towards the collaborative development of machine learning techniques in planning support systems - a Sydney example

Oliver Lock et al.

Summary: The rise of 'big data' has advanced computational analysis techniques like machine learning and artificial intelligence, impacting urban planning with planning support systems and machine learning applications. Research focused on the role of planning support systems in participatory data analytics, co-designing machine learning systems for solving real planning issues in Sydney. Results emphasized the importance of consistency, transparency in system design, along with positive attitudes towards AI and machine learning techniques among practitioners and developers.

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE (2021)

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Review Computer Science, Artificial Intelligence

Spatiotemporal clustering: a review

Mohd Yousuf Ansari et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

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Editorial Material Computer Science, Information Systems

GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Krzysztof Janowicz et al.

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2020)

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Automated machine learning: Review of the state-of-the-art and opportunities for healthcare

Jonathan Waring et al.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2020)

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GeoAPEXOL, a web GIS interface for the Agricultural Policy Environmental eXtender (APEX) model enabling both field and small watershed simulation

Qingyu Feng et al.

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Geospatial assessment for crop physiological and management improvements with examples using the simple simulation model

Thomas R. Sinclair et al.

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Considerations for selecting a machine learning technique for predicting deforestation

Helen J. Mayfield et al.

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Strengthening Participation Using Interactive Planning Support Systems: A Systematic Review

Johannes Flacke et al.

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Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models

Zihao Wu et al.

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Prediction of Socio-Economic Indicators for Urban Planning Using VHR Satellite Imagery and Spatial Analysis

Gebhard Warth et al.

REMOTE SENSING (2020)

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GeoDenStream: An improved DenStream clustering method for managing entity data within geographical data streams

Manqi Li et al.

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Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning

Romulus Costache et al.

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Challenges of Spatial Decision-Support Tools in Urban Planning: Lessons from New Zealand's Cities

Mirjam Schindler et al.

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Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods

Haifeng Niu et al.

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Planning support science: Developments and challenges

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Avoiding the planning support system pitfalls? What smart governance can learn from the planning support system implementation gap.

Huaxiong Jiang et al.

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Life is a scene and we are the actors: Assessing the usefulness of planning support theatres for smart city planning

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Open-source planning support system for sustainable regional planning: A case study of Stockholm County, Sweden

Jessica Page et al.

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Reporting on the Performance and Usability of Planning Support Systems-Towards a Common Understanding

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Development of a flow-based planning support system based on open data for the City of Atlanta

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Spatial Decision Support Systems with Automated Machine Learning: A Review

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