Characterisation of municipal solid waste (MSW) is important for adequate decision making in the management strategy of urban MSW of a city. The objective of this study was to characterise the MSW at a landfill of Pune city, India and suggest appropriate MSW management methods. The investigations showed that biological treatment of MSW will be feasible as it contains high organic matter and moisture content. This would help to divert 70% of the total waste, if solid waste treatment facilities were provided at source, which would lead to enormous cost savings of waste collection, transport and disposal. Energy generation through incineration of MSW was not feasible. The changing pattern of characterisation of MSW in relation to socio-economic changes has been discussed. Though the study was focused on Pune city, investigations will help solid waste management planners, materials recovery facility designers and for estimation of landfill gas emission in developing metropolitan cities.

Estimation of evaporation plays a key role in managing water resources projects. Traditionally, evaporation is determined using theoretical and empirical techniques as well as by pan observations. Practically, it is difficult to install evaporation pans at every location and empirical approach like Penman's equation is data intensive. This necessitates the use of an alternative approach, which can make use of readily available data and estimate evaporation reasonably with limited data. Major objective of the present study is to estimate evaporation using the soft computing tools of artificial neural networks (ANN) and genetic programming (GP) making use of the measured climatic parameters and to compare the results with traditional empirical techniques. The results indicate that the models developed using the soft computing tools of ANN and GP worked reasonably well for estimation of evaporation compared to empirical methods. GP works slightly better for higher values of pan evaporation compared to ANN.