Drought Analysis and Water Resources Management Inspection in Euphrates–Tigris Basin

Growing population, increasing basin development, and progressively declining water supplies are typical water resources issues in the Middle East. Drought is one of the most damaging climate‐related hazards that affect more people than any other. For identifying drought‐prone areas in the Euphrates–Tigris Basin, multifold aspects of drought and its features such as the frequency of drought occurrence and its spatial distribution were assessed. The long‐term precipitation data were collected from different meteorological stations of Turkey and Iran, and standard precipitation index (SPI) was calculated. Due to the lack of raw data, the literature works on drought were used in Syria and Iraq to obtain a drought perception in these countries. Moreover, the policy of water resources management and the hydraulic works in these regions were considered. The results show significant changes in the precipitation in these regions over the past decades. The projects undertaken in the basin are not in line with the principles of integrated water resources management and intensify the drought and caused marshland demise in the downstream of the basin. The results of a comprehensive analysis of precipitation variation and water management in this research can alter the policy of water resources management in order to avoid drought in the basin

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results