Algorithms in low-code-no-code for research applications: A practical review

F Sufi - Algorithms, 2023 - mdpi.com
Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20
years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub …

AI-enabled strategies for climate change adaptation: protecting communities, infrastructure, and businesses from the impacts of climate change

H Jain, R Dhupper, A Shrivastava, D Kumar… - Computational Urban …, 2023 - Springer
Climate change is one of the most pressing global challenges we face today. The impacts of
rising temperatures, sea levels, and extreme weather events are already being felt around …

A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset

HAH Al-Najjar, B Pradhan, G Beydoun, R Sarkar… - Gondwana …, 2023 - Elsevier
As artificial intelligence (AI) techniques are becoming more popular in landslide modeling, it
is important to understand how decisions are made. Fairness, and transparency becomes …

Landslide susceptibility map** using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA

AM Youssef, B Pradhan, A Dikshit… - Bulletin of Engineering …, 2022 - Springer
To be proactive in mountain hazard mitigation, landslide disaster assessments are
becoming increasingly urgent. In this study, three modeling techniques, namely, support …

CAS landslide dataset: a large-scale and multisensor dataset for deep learning-based landslide detection

Y Xu, C Ouyang, Q Xu, D Wang, B Zhao, Y Luo - Scientific Data, 2024 - nature.com
In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset
for deep learning-based landslide detection, developed by the Artificial Intelligence Group at …

Handling data imbalance in machine learning based landslide susceptibility map**: a case study of Mandakini River Basin, North-Western Himalayas

SK Gupta, DP Shukla - Landslides, 2023 - Springer
Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility map** is a collection of landslide causative factors …

Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides

Q Liu, A Tang, D Huang - Catena, 2023 - Elsevier
Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …