Titlebook: Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation; Theory and Practice Ravinesh C. Deo,Pijush Samui,Zaher Mundh

Hoover

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declamation

agglomerate

Energy Dissipation in Rough Chute: Experimental Approach Versus Artificial Intelligence Modeling,ccurs at the slopes of 16.4 and 35°, respectively. CCNN model yields excellent performance for predicting of relative energy loss (.?=?0.983 and RMSE?=?0.02). The methodologies are adaptable in real decision support systems for disaster risk mitigation.

半球

Spatial Modeling of Soil Erosion Susceptibility with Support Vector Machine,the SVM model performed well in both training (AUC?=?84.1% and TSS?=?0.651) and validation (AUC?=?81.2% and TSS?=?0.62) steps. Therefore, SVM is capable to accurately model soil erosion in data-scarce regions. The adopted methodology can be used as an efficient approach for land-use planning and adopting mitigation strategies.

不能妥協(xié)

2363-7633 case studies on advanced data analytics for real-life decisiThis book highlights cutting-edge applications of machine learning techniques?for disaster management by monitoring, analyzing, and forecasting hydro-meteorological variables.?Predictive modelling is a consolidated discipline used to forewa

6Applepolish

Bayesian Markov Chain Monte Carlo-Based Copulas: Factoring the Role of Large-Scale Climate Indices monthly FI that can be predicted at least four months ahead using SOI information. These advanced flood prediction models, presented in this chapter, are indeed imperative tools for civil protection and important to early warning and risk reduction systems.

易于交談

折磨

2363-7633 seful reference for graduate student, academics, practicing scientists and professionals of disaster management, artificial intelligence, and? environmental sciences.?978-981-15-5774-3978-981-15-5772-9Series ISSN 2363-7633 Series E-ISSN 2363-7641

Peristalsis

Intelligent Data Analytics for Decision-Support Systems in Hazard MitigationTheory and Practice

MERIT