This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009–2019). This paper has been motivated by the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research.

Seated at the foot of the Misti volcano in an area prone to intense seasonal rains and earthquakes, the city of Arequipa is highly vulnerable to natural disasters. During the rainy season, intense storms create large volumes of runoff that rush through the city's ephemeral streams, known locally as torrenteras. Episodic flows in these torrenteras have caused flooding, damage to bridges, homes, and other infrastructure, and caused many deaths. In recent years, while unprecedented rain events have caused extreme disasters, the city's population has continued to expand into these channels by creating informal or illegal settlements. Currently, detailed hazard maps of flood‐prone areas surrounding the torrenteras are not available to stakeholders in Arequipa. In this study, hydrologic and hydraulic models were combined to assess flash flood hazards, including inundation, velocity hazards, and slope instability hazards. Hydrologic models were created using satellite precipitation data and terrain‐sensitive, gridded climate maps to characterize flow within six torrenteras in Arequipa. These flows were used in conjunction with elevation data and data collected in the field using an online mobile application system to develop a hydraulic model of these flood events. Hydraulic model outputs were used to determine flood hazards related to inundation, velocity affecting human stability, and slope instability in case study areas of the torrenteras. We then discuss how this information can be used by disaster risk management groups, water authorities, planners and municipalities, and community groups.

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.