Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Effect of gravity framing on the overstrength and collapse capacity of steel frame buildings with perimeter special moment frames

This paper investigates the effect of the gravity framing system on the overstrength and collapse risk of steel frame buildings with perimeter special moment frames (SMFs) designed in North America. A nonlinear analytical model that simulates the pinched hysteretic response of typical shear tab connections is calibrated with past experimental data. The proposed modeling approach is implemented into nonlinear analytical models of archetype steel buildings with different heights. It is found that when the gravity framing is considered as part of the analytical model, the overall base shear strength of steel frame buildings with perimeter SMFs could be 50% larger than that of the bare SMFs. This is attributed to the gravity framing as well as the composite action provided by the concrete slab. The same analytical models (i) achieve a static overstrength factor, Ω_s larger than 3.0 and (ii) pass the collapse risk evaluation criteria by FEMA P695 regardless of the assigned total system uncertainty. However, when more precise collapse metrics are considered for collapse risk assessment of steel frame buildings with perimeter SMFs, a tolerable probability of collapse is only achieved in a return period of 50 years when the perimeter SMFs of mid‐rise steel buildings are designed with a strong‐column/weak‐beam ratio larger than 1.5. The concept of the dynamic overstrength, Ω_d is introduced that captures the inelastic force redistribution due to dynamic loading. Steel frame buildings with perimeter SMFs achieve a Ω_d > 3 regardless if the gravity framing is considered as part of the nonlinear analytical model representation. Copyright © 2014 John Wiley & Sons, Ltd.     

Monitoring disasters with a constellation of satellites – type examples from the International Charter ‘Space and Major Disasters’

In the case of a major disaster, information derived from satellite observation is not only highly useful, it may at times be indispensable because of the damage caused by the disaster to ground infrastructure. The International Charter ‘Space and Major Disasters’ (‘the Charter’) has been one of the primary sources of satellite data for the past 11 years to cover events like floods, fires, tsunamis, ocean storms, earthquakes, volcanic eruptions and oil spills. With the growing membership of the Charter, an increasingly large number of sensors are now available, which can be planned with the required temporal frequency and spectral range to cover a disaster event. Some of the type Charter activation cases are reported in this article to demonstrate the innovative use of multi-satellite imagery for disaster response.     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Microcracks and Overpressure- Induced Fractures

The microfractures and natural fractures studies have a great significance in the hydrocarbon accumulation exploration and can be a good supporter for wide geophysical study on the major structures. This paper is a brief review on microcracks and overpressure- induced fracture. It is to discuss different views on the mechanism of fractures in the subsurface of the earth.     

Smoothed Particle Hydrodynamics for modelling landslide–water interaction problems

Landslides - This work aims to develop a Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) in-house code for modelling landslide–water interaction problems. While we consider the...     

Compression Behaviour of Chlef Sand and Transition of Fines Content Using Pressure-Dependent Maximum Void Ratios of Sand

Geotechnical and Geological Engineering - This paper presents a study on Chlef sand to examine the effect of fines content (Fc), relative density (RD) and initial conditions on the compressibility...     

Anticipated economic costs and benefits of ratification of the Kyoto Protocol by the State of Qatar

Abstract

Further ratification of the Kyoto Protocol by non-Annex 1 countries such as the State of Qatar will not affect the entrance into force of the Treaty; however, ratification remains an important decision due to other considerations, primarily the economic costs and benefits associated with ratification. As a member of the Organization of Petroleum Exporting Countries (OPEC), Qatar's economic position is closely allied with revenue generated from its oil and natural gas resources. Qatar expects adverse impacts from implementing the Kyoto Protocol, though the estimated magnitude varies enormously with different models. Also, the impacts depend significantly on how the implementation is done; for example, the kind of policies that other countries use. Qatar is able to counter adverse impacts by exploiting its greater share of natural gas and developing energy-intensive industries that produce nonenergy goods such as steel, petrochemicals, and chemicals. Furthermore, it is clear that clauses in the Protocol can be developed to protect OPEC interests, and if Qatar does not ratify the Protocol, it will be excluded from the subsequent rule-making processes. On balance, there are benefits to ratifying the Treaty, and there is also a strong need for extensive further research.