Governing the water conservation in the Urmia Lake Basin: addressing macro systems’ fit and micro users’ behavior

To change unadapted water governing systems, and water users’ traditional conducts in line with climate change, understanding of systems’ structures and users’ behaviors is necessary. To this aim, comprehensive and pragmatic research was designed and implemented in the Urmia Lake Basin where due to the severe droughts, and human-made influences, especially through the agricultural development, the lake has been shrunken drastically. To analyze the water governance and conservation issues in the basin, an innovative framework was developed based on mathematical physics concepts and pro-environmental behavior theories. Accordingly, in system level (macro/meso), the problem of fit of the early-shaped water governing system associating with the function of “political-security” and “political-economic” factors in the basin was identified through mean-field models. Furthermore, the effect of a “political-environmental” factor, the Urmia Lake Restoration Program (ULRP), on reforming the system structure and hence its fit was assessed. The analysis results revealed that by revising the provincial boundaries (horizontal alternation) for the entity of Kurdistan province to permit that interact with the headquarter of West Azerbaijan province for its water demand-supply initiatives, the system fit can increase. Also, the constitution of the ULRP (vertical arrangement) not only could increase the structural fit of the water governing system to the basin, but also significantly could enhance the system fit through its water-saving policy. Besides, in individual level (micro), the governing factors of water conservation behavior of the major users/farmers were identified through rational and moral socio-psychological models. In rational approach, incorporating PMT and TPB, the SEM results demonstrated that “Perceived Vulnerability”, “Self-Efficacy”, “Response Efficacy”, “Response Cost”, “Subjective Norms” and “Institutional Trust” significantly affect the water-saving intention/behavior. Likewise, NAM based analysis as a moral approach, uncovered the significant effects of “Awareness of Consequences”, “Appraisal of Responsibility”, “Personal Norms” as well as “Place Attachment” and “Emotions” on water-saving intention.

On the Democratization of AI: Bridging the Gap with AutoML

Riding the wave of recent groundbreaking achievements, artificial intelligence (AI) is currently the buzzword on everybody’s lips and, allowing algorithms to learn from historical data, Machine Learning (ML) emerged as its pinnacle. The multitude of algorithms, each with unique strengths and weaknesses, highlights the absence of a universal solution and poses a challenging optimization problem. In response, automated machine learning (AutoML) navigates vast search spaces within minimal time constraints. By lowering entry barriers, AutoML emerged as promising the democratization of AI, yet facing some challenges. In data-centric AI, the discipline of systematically engineering data used to build an AI system, the challenge of configuring data pipelines is rather simple. We devise a methodology for building effective data pre-processing pipelines in supervised learning as well as a data-centric AutoML solution for unsupervised learning. In human-centric AI, many current AutoML tools were not built around the user but rather around algorithmic ideas, raising ethical and social bias concerns. We contribute by deploying AutoML tools aiming at complementing, instead of replacing, human intelligence. In particular, we provide solutions for single-objective and multi-objective optimization and showcase the challenges and potential of novel interfaces featuring large language models. Finally, there are application areas that rely on numerical simulators, often related to earth observations, they tend to be particularly high-impact and address important challenges such as climate change and crop life cycles. We commit to coupling these physical simulators with (Auto)ML solutions towards a physics-aware AI. Specifically, in precision farming, we design a smart irrigation platform that: allows real-time monitoring of soil moisture, predicts future moisture values, and estimates water demand to schedule the irrigation.