[PDF][PDF] Is deep learning on tabular data enough? An assessment
It is critical to select the model that best fits the situation while analyzing the data. Many
scholars on classification and regression issues have offered ensemble techniques on …
scholars on classification and regression issues have offered ensemble techniques on …
Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus
The target of the current paper was to examine the performance of three Markovian and
seasonal based artificial neural network (ANN) models for one-step ahead and three-step …
seasonal based artificial neural network (ANN) models for one-step ahead and three-step …
Multi-timescale drought prediction using new hybrid artificial neural network models
In this study, new hybrid artificial neural network (ANN) models were used for predicting the
groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization …
groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization …
[HTML][HTML] Artificial intelligence based ensemble modeling for multi-station prediction of precipitation
The aim of ensemble precipitation prediction in this paper was to achieve the best
performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based …
performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based …
Effects of direct input–output connections on multilayer perceptron neural networks for time series prediction: L. Wang
Y Wang, L Wang, Q Chang, C Yang - Soft Computing, 2020 - Springer
Feedforward neural network prediction is the most commonly used method in time series
prediction. In view of the low prediction accuracy of the conventional BPNN model when the …
prediction. In view of the low prediction accuracy of the conventional BPNN model when the …
The rainfall-induced landsliding in Western Serbia: A temporal prediction approach using Decision Tree technique
This paper focuses on modeling rainfall-induced massive landsliding in the Western Serbia
in the 2001–2014 period. The motivation for conducting the study was the rainfall-induced …
in the 2001–2014 period. The motivation for conducting the study was the rainfall-induced …
Computational decision intelligence approaches for drought prediction: A review
M Pakdaman, M Kouhi - Uncertainty in Computational Intelligence-Based …, 2025 - Elsevier
Drought is widely recognized as one of the devastating natural disasters, causing
catastrophic consequences. This recurring climatic phenomenon affects different aspects of …
catastrophic consequences. This recurring climatic phenomenon affects different aspects of …
Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach
This study aimed at time-space estimations of monthly precipitation via a two-stage
modeling framework. In temporal modeling as the first stage, three different Artificial …
modeling framework. In temporal modeling as the first stage, three different Artificial …
Deformation prediction and monitoring using real-time WSN and machine learning algorithms: A review
M Fayaz, JA Qurashi - Cognitive Machine Intelligence, 2025 - taylorfrancis.com
This chapter focuses on the development of an Early Warning System (EWS) for landslides,
which have become increasingly frequent due to changes in precipitation patterns …
which have become increasingly frequent due to changes in precipitation patterns …
Deformation prediction and monitoring using real-time WSN and machine learning algorithms
M Fayaz, JA Qurashi - Cognitive Machine Intelligence …, 2024 - books.google.com
A landslide is a movement of material like rocks, soil, and trees on steep slopes. It occurs
when the force of gravity surpasses the strength of the materials on the slope, often resulting …
when the force of gravity surpasses the strength of the materials on the slope, often resulting …