Drought events rank as the second most costly natural disasters within the French legal framework of the natural disaster compensation scheme. A critical aspect of the national compensation scheme involves cities submitting requests for the government declaration of natural disaster for a drought event as a key step. We take on the challenge of forecasting which cities will submit such requests. The problem can be tackled as a classification task, leveraging the power of classification algorithms. Taking a slightly different perspective, we introduce an alternative procedure that hinges on optimal transport theory and iPiano, an inertial proximal algorithm for nonconvex optimization. The optimization problem is designed so as to yield a sparse vector of predictions because it is known that relatively few cities will submit requests. Additionally, we develop a hybrid procedure that synergistically combines and utilizes predictions derived from both perspectives, resulting in enhanced forecasting accuracy. A simulation study illustrates the procedures. The real data application is presented and discussed in details. The convergence of the iPiano algorithm is established, using the notion of o-minimal structures from the field of tame geometry. A simulation study illustrates the procedures. The real data application is presented and discussed in details. The convergence of the iPiano algorithm is established, using the notion of o-minimal structures from the field of tame geometry.