Climate driven drought risk and machine learning approaches for urban resilience and sustainable water governance

Drought is thought of as one of the gravest climate-related hazards of the agro-dependent regions facing water stress like Pakistan where socio-economic stability and food security are under threat because of the increasing hydro-climatic variability. In this paper, a more advanced drought risk forecasting model has been analyzed, which integrates the multi-scale Standardized Precipitation Indices, climatic zoning based on the Köppen-Geiger classification, as well as the deep learning model to predict and identify different drought dynamics in various climatic regions. The spatiotemporal pattern analysis of droughts is considered to be the SPI of 1, 3, 6, 9, and 12-month. Besides that, performance of hybrid model (RNN, LSTM, BiLSTM and CNN-LSTM) has been compared with the old algorithm (SVM and empirical model using Penman-Monteith. The results indicate that the intensity and the length of droughts have been increasing at a high pace within the semi-arid and coastal desert regions of the country. Long-term trends in droughts were best modeled by SPI-9 and SPI-12. The models of deep learning are significantly superior to the baseline methods. The CNN-LSTM would be the best to use in the long-term prediction, whereas BiLSTM appears to be more efficient in the short-term predictions. These outcomes indicate that deep neural networks can learn non-linear climatic dynamics besides providing action on-lead-time drought risk management data. The proposed prediction system also forms the basis of the anticipatory governance in providing information on crop planning, deferral of irrigation, regulation of abstraction of groundwater, and drought-contingency plan. To improve the progress of the climate-risk-intelligence developments, to be incorporated in the policy-making, the strengthening of the national early-warning possibilities and resilience planning can be applied. In the light of an interdisciplinary effort of applying climate diagnostics in predictive analytics, the research offers a scaling route to the utilization of information in both tracking drought and climate-adjusting water management in not only Pakistan but also other disaster-prone regions in South Asia.