Extensive brackishwater aquaculture, which is a dominant land-based aquaculture system in Indonesia, has experienced variable success in most farming locations in the country due to poor understanding of spatial assessment of environmental factors and rudimentary site selection criteria. Despite tremendous potential, the application of GIS and remote sensing in spatial assessment has tended to focus on Boolean (Crisp) logic that is often unable to effectively handle the complexity and spatial variability of key environmental factors for the development of aquaculture. This study explored the possibility of integrating fuzzy logic techniques into GIS and remote sensing technology to generate more robust mapping protocols in aquaculture, compensating for the disadvantages of the Crisp method. Two models were developed in two different provinces in Indonesia to spatially assess soil and hydrological constraints on extensive brackishwater aquaculture. The soil assessment focussed on acid sulfate soils (ASS) and sandy-textured sediments in Aceh, and the hydrological study focused on investigating important wave parameters that influence the suitability of coastal areas for siting extensive pond units in South Sulawesi. The study showed that fuzzy-based classification methods, integrated into the image analysis, was highly effective in identifying existing and potential pond areas for extensive brackishwater aquaculture compared to the best result of the commonly used Crisp method. By addition of one or more key environmental variables of ASS into the fuzzy-classified existing and potential ponds areas, a very robust predictive tool to identify potential ponds areas affected by ASS in Kembang Tanjung, Aceh was developed. A more detailed assessment of ASS developed in this study also successfully highlighted the severity of sandy-soils and identified them as another key soil variable that has and will severely impact on pond productivity. The second model developed by the study enables fuzzy logic to be integrated into GIS to predict the possible areas impacted by moderate to high energy wave conditions and possible ways of minimising their direct and indirect impacts. The models developed in this study were shown to work well in both study sites and can be applied elsewhere. The mapping outputs are easy to interpret even by stakeholders with no prior training in map reading. Overall, the models have the potential to reduce planning errors and to improve decision making in aquaculture provided that quality data sources are used.

Extensive brackishwater aquaculture, which is a dominant land-based aquaculture system in Indonesia, has experienced variable success in most farming locations in the country due to poor understanding of spatial assessment of environmental factors and rudimentary site selection criteria. Despite tremendous potential, the application of GIS and remote sensing in spatial assessment has tended to focus on Boolean (Crisp) logic that is often unable to effectively handle the complexity and spatial variability of key environmental factors for the development of aquaculture. This study explored the possibility of integrating fuzzy logic techniques into GIS and remote sensing technology to generate more robust mapping protocols in aquaculture, compensating for the disadvantages of the Crisp method. Two models were developed in two different provinces in Indonesia to spatially assess soil and hydrological constraints on extensive brackishwater aquaculture. The soil assessment focussed on acid sulfate soils (ASS) and sandy-textured sediments in Aceh, and the hydrological study focused on investigating important wave parameters that influence the suitability of coastal areas for siting extensive pond units in South Sulawesi. The study showed that fuzzy-based classification methods, integrated into the image analysis, was highly effective in identifying existing and potential pond areas for extensive brackishwater aquaculture compared to the best result of the commonly used Crisp method. By addition of one or more key environmental variables of ASS into the fuzzy-classified existing and potential ponds areas, a very robust predictive tool to identify potential ponds areas affected by ASS in Kembang Tanjung, Aceh was developed. A more detailed assessment of ASS developed in this study also successfully highlighted the severity of sandy-soils and identified them as another key soil variable that has and will severely impact on pond productivity. The second model developed by the study enables fuzzy logic to be integrated into GIS to predict the possible areas impacted by moderate to high energy wave conditions and possible ways of minimising their direct and indirect impacts. The models developed in this study were shown to work well in both study sites and can be applied elsewhere. The mapping outputs are easy to interpret even by stakeholders with no prior training in map reading. Overall, the models have the potential to reduce planning errors and to improve decision making in aquaculture provided that quality data sources are used.

In this modern era, energy is a key element required for sustainable development and prosperity of a society. Pakistan is an energy deficient country facing problems due to the shortage of over 4000 MW of electricity. The national energy sector is heavily dependent on imported fossil-fuel resources. The energy crisis is negatively affecting all economic and business activities, and it is widely recognized as a severe obstacle to growth and poverty reduction in the country. Establishment of wind farms can help to overcome the energy crisis. In this research, a national level wind energy roadmap is developed through scenario planning. Multiple future scenarios are developed using the fuzzy cognitive maps (FCM) approach. This research has extended technology roadmapping through FCM-based scenario analysis. Building scenarios with FCM is a very new approach, and for the first time FCM-based scenarios are developed for the wind energy sector of Pakistan. Based on these multiple scenarios, a technology roadmap has been developed. This research approach is applied to the wind energy sector of Pakistan as a case study. This approach has been used to establish objectives and national targets of the roadmap. Then in a systematic way, critical roadmap barriers are identified against each scenario, and appropriate action items have been proposed to overcome barriers and promote deployment of wind energy projects in Pakistan. The objectives and targets of the roadmap have been translated into action items. The technology roadmap has four layers: strategic objectives, targets, barriers, and action items. Expert panels have been utilized to develop scenarios and technology roadmaps. Validation of this research is also carried out using experts. This new approach has helped to develop a robust roadmap and enabled anticipation of a wide range of possible future outcomes. This research fills an important gap by combining scenario planning and technology roadmapping techniques in future studies, and it has enhanced flexibility of the developed roadmap. Moreover, for the first time multiple and plausible FCM-based scenarios are developed, which combine the benefits of both qualitative and quantitative analysis. Moreover, the technology roadmap for the wind energy sector of Pakistan is developed with a comprehensive study of practical obstacles and barriers towards deployment of wind energy technology. The research findings suggest that policy, financial, economic, lack of competition with conventional power plants, and technical are the most critical barriers towards deployment of wind energy projects in the country. Appropriate action items required to overcome the roadmap barriers against each scenario are also proposed in the developed roadmap. The experts also assigned responsibilities for the key roadmap action items to the major stakeholders.