A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

Download

index.pdf

Date

2022-04-01

Author

Basagaoglu, Hakan
Chakraborty, Debaditya
Do Lago, Cesar
Gutierrez, Lilianna
ŞAHİNLİ, MEHMET ARİF
Giacomoni, Marcio
Furl, Chad
Mirchi, Ali
Moriasi, Daniel
Şengör, Sema Sevinç

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

285
views

119
downloads

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.

Subject Keywords

explainable artificial intelligence, multidimensional data, nonlinearity, explanatory methods, hydroclimatic applications, CLIMATE-CHANGE, BLACK-BOX, REFERENCE EVAPOTRANSPIRATION, PRECIPITATION, PREDICTION, MODEL, WATER, CONSUMPTION, IMPUTATION, DECISIONS

URI

https://hdl.handle.net/11511/97279

Journal

WATER

DOI

https://doi.org/10.3390/w14081230

Collections

Department of Environmental Engineering, Article

Suggestions

OpenMETU
Core

Generation and modification of 3D models with deep neural networks Öngün, Cihan; Temizel, Alptekin; Department of Information Systems (2021-9) Artificial intelligence (AI) and particularly deep neural networks (DNN) have become very hot topics in the recent years and they have been shown to be successful in problems such as detection, recognition and segmentation. More recently DNNs have started to be popular in data generation problems by the invention of Generative Adversarial Networks (GAN). Using GANs, various types of data such as audio, image or 3D models could be generated. In this thesis, we aim to propose a system that creates artificial...
Evaluating the convergence of high-performance computing with big data, artificial intelligence and cloud computing technologies Dildar Korkmaz, Yeşim; Eren, Pekin Erhan; Kayabay, Kerem; Department of Information Systems (2023-1-24) The advancements in High-Performance Computing (HPC), Big Data, Artificial Intelligence (AI), and Cloud Computing technologies have led to a convergence of these fields, resulting in the emergence of significant improvements for a wide range of fields. Identifying the state of development of technology convergence and forecasting promising technology convergence is critical for both academia and industry. That's why technology assessment and forecasting for HPC-Big Data-AI-Cloud Computing convergence is nee...
A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009) A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...
A Review on Data Mining and Continuous Optimization Applications in Computational Biology and Medicine Weber, Gerhard Wilhelm; Ozogur-Akyuz, Sureyya; Kropat, Erik (2009-06-01) An emerging research area in computational biology and biotechnology is devoted to mathematical modeling and prediction of gene-expression patterns; it nowadays requests mathematics to deeply understand its foundations. This article surveys data mining and machine learning methods for an analysis of complex systems in computational biology, It mathematically deepens recent advances in modeling and prediction by rigorously introducing the environment and aspects of errors and uncertainty into the genetic con...
A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...

Citation Formats

H. Basagaoglu et al., “A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications,” WATER, vol. 14, no. 8, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97279.