BUILDING PREDICTIVE MODELS TO ASSESS DEGRADATION OF SOIL ORGANIC MATTER OVER TIME USING REMOTE SENSING DATA by Abdulsalam Aljumaily, Ammar Kashmolaa

“…The results of the study showed the possibility of applying predictive models to Satellite data for a particular area and for previous years to give results with high spatial accuracy (R2 = 0.9581). …”
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Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles. by Alexandra V Kulinkina, Yvonne Walz, Magaly Koch, Nana-Kwadwo Biritwum, Jürg Utzinger, Elena N Naumova

“…We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.<h4>Methodology</h4>Schistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10-30 m). …”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…The sampling strategy was designed to ensure comprehensive data collection, allowing for robust model training and validation. MaxEnt, which is a presence-only model, outperformed both the GARP and logistic regression models in predicting suitable habitats for Astragalus sp. …”
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EMGP-Net: A Hybrid Deep Learning Architecture for Breast Cancer Gene Expression Prediction by Oumeima Thâalbi, Moulay A. Akhloufi

“…Recent studies have used whole-slide images combined with spatial transcriptomics data to predict breast cancer gene expression. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…First, the time series of data stream used for prediction is subjected to two-stage weighting processing,and then the processed time series and its dependent spatial topology information are input into the network model for spatiotemporal feature extraction. …”
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High-resolution spatial prediction of anemia risk among children aged 6 to 59 months in low- and middle-income countries by Johannes Seiler, Mattias Wetscher, Kenneth Harttgen, Jürg Utzinger, Nikolaus Umlauf

“…Methods Employing full probabilistic Bayesian distributional regression models, the research accurately predicts age-specific and spatially varying anemia risks. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. …”
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A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model by Lei HAN, Xinxin QI, Tianjun WANG, Chunmin PEI, Xudong WANG

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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. …”
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Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. …”
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FibroRegNet: A Regression Framework for the Pulmonary Fibrosis Prognosis Prediction Using a Convolutional Spatial Transformer Network by Pardhasaradhi Mittapalli, V. Thanikaiselvan

“…Predicting the growth of idiopathic pulmonary fibrosis (IPF) is crucial for effectively treating patients affected by the disease. …”
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Characteristics of Spatial and Temporal Evolution of Coastal Wetland Landscape Patterns and Prediction Analysis—A Case Study of Panjin Wetland, China by Qian Cheng, Ruixin Chen, Wei Xu, Meiqing Wang

“…For this research, we quantified the landscape type changes in Panjin Wetland from 1992–2022, and analyzed the interaction between the combined PLUS and InVEST models to predict the future evolution of spatial and temporal patterns of habitat quality (HQ) and landscape patterns in Panjin Wetland. …”
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A Review of Wind Power Prediction Methods Based on Multi-Time Scales by Fan Li, Hongzhen Wang, Dan Wang, Dong Liu, Ke Sun

“…Common classification angles of wind power prediction methods are outlined. By synthesizing existing approaches through multi-time scales, from the ultra-short term and short term to mid-long term, the review further deconstructs methods by model characteristics, input data types, spatial scales, and evaluation metrics. …”
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Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model by Yalei Yang, Hong Wang, Xiaobing Li, Tengfei Qu, Jingru Su, Dingsheng Luo, Yixiao He

“…The projected landscape diversity risk warning for 2025 mirrors the historical spatial data, with a notable reduction in local disparities and an overall decrease in the average value by 2.73%. …”
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Development of an AI model for DILI-level prediction using liver organoid brightfield images by Shiyi Tan, Yan Ding, Wei Wang, Jianhua Rao, Feng Cheng, Qiuyin Zhang, Tingting Xu, Tianmu Hu, Qinyi Hu, Ziliang Ye, Xiaopeng Yan, Xiaowei Wang, Mingyue Li, Peng Xie, Zaozao Chen, Geyu Liang, Yuepu Pu, Juan Zhang, Zhongze Gu

“…Here we show a drug-induced liver injury (DILI) level prediction model using HLO brightfield images (DILITracer) considering that DILI is the major causes of drug withdrawals. …”
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Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda by Jean Marie Vianney Ntamwiza, Hannibal Bwire

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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R²) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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Predictive Modeling the Turbidity Response in Al-Saray Water Distribution Network in Najaf Governorate/Middle of Iraq, Using PODDS Model by Abed Zahraa H., Jasem Hayder M., Mohammed Hayder S.

“…Reducing water turbidity is one of the main issues the water industry is currently experiencing. The ability to predict the spatial probability and intensity of discoloration events in distribution systems can lead to the adoption and improvement of proactive operation and maintenance strategies to reduce turbidity. …”
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Exploring the Role of Digital Economy in Energy Optimization of Manufacturing Industry Under the Constraint of Carbon Reduction? Based on Spatial Panel Threshold Hybrid Model by Lingyao Wang, Taofeng Wu, Fangrong Ren

“…Based on panel data from 30 provinces in mainland China from 2016 to 2022, this research investigates the spatial spillover effect and nonlinear impact of the digital economy on the energy optimization of the manufacturing industry using the spatial econometric and panel threshold model. …”
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