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Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

To date, a few attempts have been made to predict the annual solid waste generation in developing countries. This paper presents modeling of annual solid waste generation using Modified ANFIS, it is a systematic approach to search for the most influencing factors and then modify the ANFIS structure to simplify the model. The proposed method can be used to forecast the waste generation in such developing countries where accurate reliable data is not always available. Moreover, annual solid waste prediction is essential for sustainable planning.

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Abstract. The implementation of green roofs or vegetated roof as a sustainable tool to mitigate the Urban Heat Island effect is relatively new in Malaysia. Although it has not been tested on an urban scale, many research findings have indicated that green roofs can contribute towards enhancing the environmental and aesthetical quality of the built environment. It was hypothesized that the low application of green roofs in the Malaysian construction industry is due to the lack of awareness, understanding and experience in its benefits especially among building practitioners. As a result, this research was initiated to determine the perception and understanding of Malaysian architects in green roofs implementation issues, as well as to identify their level of acceptance and readiness. This paper reviews practices and different research approaches in understanding the factors that influence architect's perception towards the implementation of green roofs in the Malaysian construction industry. Architects were chosen as the only respondents due to their intensive involvement in the conceptualisation, planning, design and construction stage of a built environment project. Extensive literature review was conducted to explore past experiences in green roof implementation and to develop the theoretical framework for this research.

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