Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.
After wheat, rice is one of the most important agricultural products in the world, and Iran has a special position here with annual production of more than 2 million t of rice. Evaluation of crop yield has an important role in agricultural policy making due to different conditions and restrictions. Estimating rice yield is a key factor in food security. Any change in the effective parameters can cause changes in rice yield and therefore the food security of the population will be affected. In this study, rice crop yield was estimated by artificial neural networks (ANNs) and ANN‐genetic programming (GP) in 2011 and 2015. Rainfall, permeability, soil texture, land type, evapotranspiration and inlet and inflow and outflow water to paddy lands were used as inputs. The results showed that the ANN‐GP with a root mean square error (RMSE = 80.8 kg ha‾¹) and a correlation coefficient (CC = 0.91) was more accurate than the stand‐alone ANN (with RMSE = 139 kg ha‾¹ and CC = 0.67). Finally, the effect of each input parameter on rice yield was evaluated. Irrigation, drainage and soil type parameters had the best impact rank, with 36, 28 and 31%, respectively. Therefore, the proposed method can act as an efficient tool in estimating rice yield and help decision makers to manage and develop the agricultural system.
In this paper, the mechanical properties of randomly shaped microstructures containing two different elastic materials are investigated. Representative volume elements (RVE) containing random tessellations were created using a random generating procedure. The procedure divides the RVE surface by Voronoi tessellations and the elastic behavior of the surface is analyzed under tensile and shear deformations using the finite element method (FEM). Components of stress tensor for each element obtained from FE analysis were used to compute the overall elastic properties of the microstructure. Percolation threshold was defined based on the instantaneous gradient of the tensile and shear modulus diagrams. Numerical results reveal that the percolation thresholds in tensile and shear modes for isotropic RVE are almost the same while there is a remarkable difference between percolation thresholds for an anisotropic case. Furthermore, in the procedure performed in this study, a distinct inconsistency in elastic properties of anisotropic microstructure in longitudinal and transverse directions is observed. The mentioned method presents a paradigmatic overview for generating random isotropic and anisotropic tessellations with different aspect ratios on microstructures and evaluating their overall properties and percolation limit for them.
Enhancing the mechanical properties of materials by adding inclusions (particles) into microstructure is a favorite research area for material science researchers. In this paper, effects of different geometrical shapes of inclusions on overall elastic properties of a microstructure with regular tessellations were investigated. Finite element method (FEM) was used to analyze the stretching and shearing behavior of the microstructure including particles with different geometrical shapes. The results show that the dependency of tensile and shearing properties of the microstructure on volume fraction of particles can completely have different trends in some ranges of volume fractions. Besides, it was observed that each regular tessellation can have a different effect in enhancement of mechanical properties. Also the effects of mechanical percolation on the elastic properties of microstructures were investigated. The presented approach opens a systematic method for investigating the enhanced properties of microstructures with different shapes of tessellations.
Throughout history, natural events such as floods, droughts, fires, lightning, and storms have caused significant losses of life and property. To mitigate the hazardous consequences of such events, or ‘failures’ (as they are referred to), a number of questions can be asked, such as: ‘What are the causes of these events?’; ‘What natural factors cause these events?’; ‘What is the human role in the occurrence of these events?’; ‘Who is to blame for such events?’; and ‘What actions should be taken to prevent such events from happening?’ The forensic engineering approach allows us to answer these questions. Forensic engineering, a term developed in recent years, allows us to identify the causes of events by looking back and analyzing the relationship between an event's causes and their consequences; it is a useful tool for determining the natural or human causes of events that lead to disasters. Forensic hydrology is a branch of forensic engineering and applies directly to floods and droughts but is not limited to these events. Forensic hydrology is also used for the historical assessment and analysis of events such as water pollution, drying of lakes and rivers, the drying up (or significant reduction in the water table) of wells, and the infiltration of saline water into freshwater. Forensic hydrology analyzes event evidence and data from a variety of perspectives. Examining the origins and mechanisms of such events to find their causes can lead to better water management, allocation and improved use, and can also help to prevent or minimize severe damage. This chapter provides an introduction to forensic engineering and describes the processes which should be followed to evaluate hazardous events.
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