Uncertainty of climate change impact on crop characteristics: a case study of Moghan plain in Iran     

Sharafati  Ahmad  Moradi Tayyebi  Mahmoud  Pezeshki  Elnaz  Shahid  Shamsuddin 《Theoretical and Applied Climatology》2022,149(1-2):603-620

Theoretical and Applied Climatology - Crop yield is one of the most critical factors in the food security chain. Climate plays a crucial role in crop water productivity in rainfed and irrigated...  相似文献   

River suspended sediment load prediction based on river discharge information: application of newly developed data mining models     

Sinan Q. Salih  Ahmad Sharafati  Khabat Khosravi  Hossam Faris  Ozgur Kisi  Hai Tao 《水文科学杂志》2020,65(4):624-637

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.  相似文献   

Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments     

Sinan Q. Salih  Ahmad Sharafati  Isa Ebtehaj  Hadi Sanikhani  Ridwan Siddique  Ravinesh C. Deo 《水文科学杂志》2020,65(7):1145-1157

ABSTRACT

Climate patterns, including rainfall prediction, is one of the most complex problems for hydrologist. It is inherited by its natural and stochastic phenomena. In this study, a new approach for rainfall time series forecasting is introduced based on the integration of three stochastic modelling methods, including the seasonal differencing, seasonal standardization and spectral analysis, associated with the genetic algorithm (GA). This approach is specially tailored to eradicate the periodic pattern effects notable on the rainfall time series stationarity behaviour. Two different climates are selected to evaluate the proposed methodology, in tropical and semi-arid regions (Malaysia and Iraq). The results show that the predictive model registered an acceptable result for the forecasting of rainfall for both the investigated regions. The attained determination coefficient (R²) for the investigated stations was approx. 0.91, 0.90 and 0.089 for Mosul, Baghdad and Basrah (Iraq), and 0.80, 0.87 and 0.94 for Selangor, Negeri Sembilan and Johor (Malaysia).  相似文献   

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis     

Ahmad Sharafati  Seyed Babak Haji Seyed Asadollah  Davide Motta 《水文科学杂志》2020,65(12):2022-2042

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.  相似文献