Sea surface temperature (SST) prediction has widespread applications in the field of marine ecology, fisheries, sports and climate change studies. At present, the real-time SST forecasts are made by numerical models which are categorically based on physics-based assumptions subjected to boundary and initial conditions. They are more suited to a large spatial region than in a specific location. In this study, location-specific SST forecasts were made by combining deep learning neural networks with numerical estimators at five different locations around India for three different time horizons (daily, weekly and monthly). Firstly, forecasts were made with traditional neural networks (NNs) and then through deep learning networks. The NNs significantly improved on the results achieved by numerical forecasts which were further enhanced by the deep learning long short-term memory (LSTM) neural network over all timescales and at all the selected sites. The model was performed successfully in terms of various statistical parameters with correlation values nearing 1.0 while minimizing the errors. Additionally, a comparative study with a linear system, the autoregressive integrated moving average with exogenous input was made. The predictive skills of deep learning LSTMs are found to be more attractive than the other existing techniques (linear or other NNs) due to their ability of learning long time dependencies and extracting features from a sample space.

In the present article, an attempt is made for simple, low-cost, and efficient removal of Auramine dye using peanut (Arachis hypogaea) shells as adsorbents. Two different forms of adsorbents distilled water washed peanut shells (DPS) and NaOH treated peanut shells (NPS) were used as adsorbents. Both the adsorbents were studied using BET, pH PZC , FTIR, SEM, TGA, and XRD characterization techniques. Adsorption parameters such as effect of contact time, pH, initial dye concentration, adsorbent dose, and temperature were also assessed. Isotherm analysis at optimum conditions showed Langmuir fitted better with a q m value of 96.15 mg/g for DPS and 294.12 mg/g for NPS; while in kinetic analysis, pseudo-second order was superior. Thermodynamics study stated that adsorption process was endothermic in nature. Overall outcomes establish that the two forms of peanut shells, DPS, and NPS were excellent in removal of Auramine dye and are low cost also as preparation cost of DPS and NPS is 68.73 INR and 106.19 INR per kg, respectively. © 2020 Water Environment Federation • Practitioner points • Removal of Auramine dye from aqueous media using different forms of peanut (Arachis hypogaea) shells as adsorbents is discussed. • q m (Langmuir) using DPS and NPS were found to be 96.15 and 294.12 mg/g, respectively. • Preparation cost of DPS was 68.73 INR per 1 kg, whereas for NPS it was 106.19 INR per 1 kg.

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Indian Road Congress (IRC)-06 specifies consideration of impact factor for dynamic analysis of bridge loaded with Class A and Class B vehicles. As per IRC-06 specifications, the impact factor is estimated based only on the span length of the bridge. In the present study, the effect of vehicle speed, road surface roughness condition and span length of the bridge on the impact factor is investigated. The dynamic response of a simply supported bridge loaded with IRC Class A and Class B vehicle loads are considered for the study. For simplifications, the vehicles are idealized as a series of moving mass spring damper system. The interaction between the bridge and vehicle is accomplished by coupling the equation of motion using the interaction force at the contact point of the wheel and bridge. The coupled equation of motion is solved using Newmark's beta method. The model is validated using past available literature. The vehicle is considered to move at a consistent speed between 20 and 100 km/h. The excitation caused due to the presence of road surface roughness at various vehicle speeds on the bridge response with span lengths varying between 20 and 100 m is investigated. Parametric studies are also conducted using frequency ratio. From the studies, it is observed that the response of the bridge subjected to IRC Class A vehicle is 40% higher than the response of the bridge subjected to IRC Class B vehicle. It is also observed from the parametric study that, the bridge response becomes critical when the vehicle moves at resonance speed and the amplitude increases with deterioration of road surface condition. The results of the impact factor study show that IRC-06 specifications underestimate the response of the bridge for high speed moving vehicles under different road surface conditions. The findings of this study can be utilized to update the IRC specifications at the time of analysis and design of both short and as well as long-span highway bridges.

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