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

FLEET

Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decompositagnostic analysis and forecast error histograms further reveal that the MEMD-M5 tree has a greater resemblance to that of the observed data supporting the outcomes of statistical evaluation. Such advancements in flood prediction models, attained through data intelligent analytical methods, are very

疲勞

Machine Learning Method in Prediction Streamflow Considering Periodicity Component,0.8?m./s for the Garhihabibullah, respectively. Including periodicity input (month number of the output) considerably improved the models’ efficiency in prediction monthly streamflow in both stations: improvements in the RMSEs of the optimal LSTM, ELM, and RF models were 20.8%, 20.5%, and 3.7 for th

大酒杯

Empirical Model for the Assessment of Climate Change Impacts on Spatial Pattern of Water Availabiliom GPCC rainfall and CRU temperature. Finally, the multi-model ensemble mean projections of rainfall and temperature of four GCMs namely MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0 and CESM1-CAM5 were used in the model for the assessment of climate change impact on water sustainability. The results reveale

guardianship

Anecdote

Oluwatobi Aiyelokun,Gbenga Ogunsanwo,Akintunde Ojelabi,Oluwole Agbede

打折

Anurag Malik,Anil Kumar,Sinan Q. Salih,Zaher Mundher Yaseen

聾子

Ramendra Prasad,Dhrishna Charan,Lionel Joseph,Thong Nguyen-Huy,Ravinesh C. Deo,Sanjay Singh

藕床生厭倦

Rana Muhammad Adnan,Mohammad Zounemat-Kermani,Alban Kuriqi,Ozgur Kisi

Exposure

Fecal-Impaction

https://doi.org/10.1007/978-981-15-5772-9disaster risk management; artificial intelligence; machine learning algorithms; environmental sciences;