I.S.G.E.: An Integrated Spatial Geotechnical and Geophysical Evaluation Methodology for Subsurface Investigations by Christos Orfanos, Konstantinos Leontarakis, George Apostolopoulos, Ioannis E. Zevgolis, Bojan Brodic

“…The automatically derived 3D models, depicting the spatial distribution of specific geotechnical parameters, provide engineers with an additional interpretation tool for better understanding the subsurface conditions of a survey area.…”
Get full text

Spatial and temporal model for WQI prediction based on back-propagation neural network, application on EL MERK region (Algerian southeast) by Saber Kouadri, Samir Kateb, Rachid Zegait

“…The test shows that the model is suitable for predicting WQI with an error rate of 9.3%.…”
Get full text

A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

“…Moreover, in terms of ocean sound speed, most of these models predict an ocean sound speed profile (SSP) at a single coordinate position, and only a few predict multi-spatial-scale SSPs. …”
Get full text

Intermodel and method comparison of mean radiant temperature from numerical weather prediction models: Evaluation of enhanced spatial resolution in Europe by Oleh SKRYNYK, Pavol NEJEDLÍK, Krzysztof BŁAŻEJCZYK

“…Mean Radiant Temperature (MRT), derivable from numerical weather prediction (NWP) models, is a critical input for many such indices. …”
Get full text

Prediction of the spatial distribution of vine weevil under climate change using multiple variable selection methods by Ga-Young Kim, Wang-Hee Lee

Get full text

Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management by Rusul Abduljabbar, Hussein Dia, Sohani Liyanage

“…Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. …”
Get full text

Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia by I.N. Gómez-Miranda, C. Restrepo-Estrada, A. Builes-Jaramillo, João Porto de Albuquerque

“…This study presents a novel landslide susceptibility model that incorporates spatial and temporal dependencies, including landslide recurrence. …”
Get full text

A machine learning-based prediction-to-map framework for rapid and accurate spatial flood prediction by Daoyang Bao, Z. George Xue, Matthew Hiatt, Kehui Xu, Courtney K. Harris, Jill C. Trepanier

“…Trained on observed data and numerical model outputs, P2M delivers rapid, accurate spatial flood predictions. …”
Get full text

Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

“…Our results show that the age of victims, fire service response times, and availability of working smoke or fire detectors were among the most important parameters for predicting fatal outcomes of structure fires. Furthermore, a predictive Bayesian regularized neural network ensemble classifier was developed to model the severity of casualties and project a spatial risk classification on the census block level. …”
Get full text

Predicting of Temporal-Spatial Sand Dunes Transition Caused by Marine Storms (Case Study: The Coast of Makran, Iran) by Soleiman PirouzZadeh, Mahmood Khosravi, Samad Fotohi

“…The aim of this paper is  modeling and prediction of changes in  land-use in 2035 by using  CA Markov model and Landsat satellite images in the West of Zarabad,( The coasts of Makran). …”
Get full text

Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial soil predictions is still under scrutiny. …”
Get full text

Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

Get full text

Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
Get full text

On Hierarchical Bayesian Spatial Small Area Model for Binary Data under Spatial Misalignment by Kindie Fentahun Muchie, Anthony Kibira Wanjoya, Samuel Musili Mwalili

“…Model-based Bayesian analysis is popular for its ability to combine information from several sources as well as taking account uncertainties in the analysis and spatial prediction of spatial data. …”
Get full text

Spatial modeling and ecological suitability of Ebola virus disease in Africa. by Lombo Baluma Didier, Lukusa Lumu Jude, Esuka Igabuchia Franck, HaoNing Wang, Xiao-Long Wang

Get full text

Spatial heterogeneity and spatial bias analyses in hedonic price models: some practical considerations by Khalid Haniza

“…Estimation of a hedonic price function using Malaysian dataset of agricultural land sale values indicates spatial disaggregation and spatial dependence. However, diagnostic tests and actual estimation of spatial models do not always provide unambiguous conclusions while predicted errors do not vary all that much from those generated by simpler models. …”
Get full text

The Impact Prediction of Income Tax Standards on Company Performance: A Hybrid Spatial Artificial Intelligence Approach by Sawsan Kareem Abdullah

“…In this study, various artificial intelligence methods such as artificial neural networks, support vector machines, deep learning, decision trees, random forests, and genetic algorithms were used in combination with spatial modeling. The results show that income tax accounting standards have a significant impact on the financial performance of companies, and the combination of artificial intelligence methods with spatial modeling significantly increases the prediction accuracy. …”
Get full text

Can spatial distribution of ungulates be predicted by modeling camera trap data related to landscape indices?... by Antonio Belda, Sandra Oltra-Crespo, Pau Miró-Martínez, Benito Zaragozí

“…In this work, we aimed at finding the best model to predict the distribution pattern of wildlife and to explain the relationship between environmental conditions with the species detected by camera traps. …”
Get full text

Orthogonal intercellular signaling for programmed spatial behavior by Paul K Grant, Neil Dalchau, James R Brown, Fernan Federici, Timothy J Rudge, Boyan Yordanov, Om Patange, Andrew Phillips, Jim Haseloff

“…We used this model to predict optimal expression levels for receiver proteins, to create an effective two‐channel cell communication device. …”
Get full text

Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. …”
Get full text