Nexus between natural and technical disaster shocks,resource depletion and growth-specific factors: evidence from quantile regression     

Akhter  Waseem  Zaman  Khalid  Nassani  Abdelmohsen A.  Abro  Muhammad Moinuddin Qazi 《Natural Hazards》2020,101(1):143-172

Natural Hazards - Debris flows are a hazardous natural calamity in mountainous regions of Nepal. Torrential rainfall within a very short period of the year is the main triggering factor for...  相似文献   

Water quality variations in different climates of Iran: toward modeling total dissolved solid using soft computing techniques     

Majid?Montaseri  Sarvin?Zaman Zad GhavidelEmail author  Hadi?Sanikhani 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(8):2253-2273

In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L^?1) and Cl (meq L^?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.  相似文献