Precipitation plays a vital role in the economies of agricultural countries, such as Pakistan. Baluchistan is the largest province in Pakistan (in terms of land) and it is facing reoccurring droughts due to changing precipitation patterns. The landscape of the province consists of rugged terrain, mountains, hills, and valleys. The torrential rains lead to devastating flash floods due to the topography of the province, which has proven to be more catastrophic in nature. It is quite intriguing to observe the changing precipitation patterns in Baluchistan. Precipitation has become less frequent but intense, resulting in flash floods and landslides, as well as damage to agriculture, infrastructure, trade, environment, and the ecosystem. Baluchistan is under a drought warning and is already facing a water crisis. This study was performed on monthly precipitation time series data obtained from the Pakistan Meteorological Department (PMD) for determining trends in precipitation from 41 years of data (1977 to 2017) over 13 selected stations in Baluchistan. Due to the non-linear nature of the precipitation data, a non-parametric Mann–Kendall (MK) test was used to determine the increasing or decreasing trends in precipitation on a monthly basis. Large-scale atmospheric circulation and climate indices that affected precipitation were considered to determine their influence on precipitation. Statistical techniques of the partial Mann–Kendall (PMK) and Pearson correlation were applied to each station to ascertain the influence on precipitation due to climatic indices.
Building information modeling gained popularity and recognition in the 2000s, making the building process productive, saving time and money, and improving collaboration. Though the building sector took some time to adopt new technology, when utilized correctly, technology can significantly boost productivity in the conventional construction industry. While BIM has significantly advanced building activities, artificial intelligence contributed by applying machine learning to increase efficiency. Since the advent of artificial intelligence for computer-aided design in the 1970s, technology has improved substantially. Numerous technical advancements are based on machine learning algorithms, propelling AI to the top of the BIM list. Presently different techniques rely primarily on human ability; these advancements and innovations are critical in the construction business. AI can be used to streamline these operations, saving costs, delays, and hazards while improving project output quality.
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