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Municipal solid waste management is a growing problem faced by several municipalities and has become a global issue, particularly in mountainous cities of developing countries. This study focuses on the existing pattern of municipal solid waste management (MSWM) and challenges in the mountainous city of Srinagar, Kashmir, India. The study aims at analysing the municipal solid waste composition, spatio-temporal variations of municipal solid waste generated and its management by using ArcGIS mapping software. The annual results of MSW composition revealed high organic waste (53.40%), followed by recyclable (16.79%) and inert waste (23.40%), respectively. Further, the ANOVA statistical analysis in SPSS software was utilized to evaluate the seasonal variations between two seasons (i.e. winter and summer). The results showed a significance between the type of MSW and the season in which MSW was generated (p = 0.001). Further, the GIS-based spatial maps were generated, viz. ward-wise location, population density, municipal solid waste generation, wastebin density and spatial distribution of open waste points with illegal dumpsites. The spatial map findings showed high-density of MSW generation between (30.04 and 35.65) Mt day −1 in administrative wards of (4, 6, 7, 16, 20, 21, 22 and 23), due to high population density. Furthermore, the forecast trend of MSW generation between 2020 and 2030 was evaluated in Minitab software. Based on forecasted findings, a total of 216,82,10 Mt MS will be generated during the next decade (i.e. 2020-2030). This high-quantity trend of MSW generation indicates that extra pressure will be on the existing MSW system. However, the problems of MSW management can be mitigated by implementing the integrated waste management plan.

Municipal solid waste (MSW) management is the most challenging environmental issue in developing countries around global municipalities. In mountainous regions, very few studies can be found and the availability of limited data, because waste management poses different challenges due to different climate, topography, and geographical conditions. This study has been conducted in three urban areas of the Kashmir region (i.e., Baramulla, Kupwara, and Bandipora) in the western Himalayas. Presently, waste management is underdeveloped and openly dumped in an unscientific manner near to the rivers, streams, and lakes, which increases the threat of contamination to ground and surface water, respectively. The present study aimed to analyze the current scenario of the MSW management system and its waste generation rates. The Physio-chemical characterization of municipal solid waste was carried out by using a quarter and cone method. Proximate analysis of samples was performed by using muffle furnace and bomb calorimeter for chemical analysis, respectively. The findings of the MSW generation of urban areas of Kashmir were approximately 181.43 Mt/day (93.73 Mt/day in Baramulla, 57.46 Mt/day in Kupwara, and 30.27 Mt/day in Bandipora), respectively. Further, the findings of MSW showed high organic waste between (10.3%-68.5%), followed by recyclable waste (12.3%-15.30%), and inert waste (8.27%-9.10%), respectively. Also, the chemical characterization results showed average moisture content varying between (47.6%-52.40%) with the lower calorific values of (1017-1175.6 kcal/kg) in the areas mentioned earlier. In conclusion, alternative treatment methods were proposed, for instance, compositing, biomethane plants, the installation of recycling plants, and upgrading the existing dumping sites to sanitary-engineered landfills.

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This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas-River Jhelum. In September 2014, a major flood occurred in the Kashmir Valley with peak discharge exceeding 115,000 m 3 /s; brought about by the multifarious interaction among atmospheric disturbances. The present study targeted the development of ANN models for flood prognosis. Thereby five neural network models were developed: Bayesian regularisation neural network, the Levenberg-Marquardt neural network, conjugate gradient neural network, scaled conjugate gradient neural network, and resilient backpropagation neural network. Levenberg-Marquardt neural network model, with the mean squared error of 0.002128 (lowest of all models) and coefficient of determination of 95.839% (highest of all models), proved to be the best model based on the statistical validation parameters. All the simulations were evaluated and curves graphed for actual versus predicted discharges for 20% of the data. These models may be used to predict floods, and take advance precautionary measures by Irrigation and Flood Control Department of the State, rather than waiting for flood hydrograph to shoot (norm as of now).

Knowledge of rainfall extremes particularly their magnitude and frequency, as embodied in Intensity-DurationFrequency (IDF) relationships and IDF curves is fundamental to many engineering problems such as design of hydraulic structures, urban drainage system, water resources projects and many others. The objective of this study is to obtain IDF relationships and curves for the Kupwara region of Kashmir valley in Jammu and Kashmir, India. Three different frequency distributions namely the Gumbel distribution, the Pearson Type III distribution and the Log-Pearson Type III distribution were fitted to the rainfall data to obtain rainfall intensities for selected return periods (2, 5, 10, 25, 50 and 100 years) and durations (0.16, 0.5, 1, 3, 6, 12 and 24 hours). Regional constants in IDF relations were found using the Sherman Morrison method and results were compared based on the Chi-square goodness-of-fit test. Results obtained using all distributions showed a similar trend. However, the Pearson Type III distribution emerges to be the best fit for the rainfall data of the region. Results revealed that higher rainfall intensities have shorter durations. Maximum rainfall intensity 81.13 mm/hr as per the best fit relation occurs with a return period of 100 years for 0.16 hours duration.

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