A critical analysis of legal responses to water pollution in Pakistan

Kiran, Ayesha; Murtiza, Ghulam; Yousaf, Amna; Hussain, Muzzamal; Al Jbawi, Entessar

doi:10.1080/23311886.2023.2254944

Cited by 1 publication

(2 citation statements)

References 37 publications

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“…Furthermore, the region cannot maintain its freshwater supplies sustainably and is already susceptible to significant water scarcity [10]. The replenishment is facilitated by the inflow of river water, primarily resulting from substantial precipitation during the monsoon period.…”

Section: Discussionmentioning

confidence: 99%

“…The worldwide precipitation over terrestrial areas has grown by 0.1 inches per decade [2]. In addition to long-term changes, scientists have associated climate change with extreme and combined phenomena, such as floods and droughts, which adversely affect society and pose challenges to water security [9][10][11]. The depletion of natural resources and increased demand for water are contributing to the reduction of water supplies, particularly in emerging nations such as Pakistan.…”

Section: Introductionmentioning

confidence: 99%

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Application of RNN-LSTM in Predicting Drought Patterns in Pakistan: A Pathway to Sustainable Water Resource Management

Shah,

Chen,

Ullah

et al. 2024

Water

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Water is a fundamental and crucial natural resource for human survival. However, the global demand for water is increasing, leading to a subsequent decrease in water availability. This study addresses the critical need for improved water resource forecasting models amidst global water scarcity concerns exacerbated by climate change. This study uses the best weather and water resource forecasting model for sustainable development. Employing a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) approach, the research enhances drought prediction capabilities by integrating secondary data of the rainfall, temperature, and ground and surface water supplies. The primary objective is to forecast water resources under changing climatic conditions, facilitating the development of early warning systems for vulnerable regions. The results from the LSTM model show an increased trend in temperature and rainfall patterns. However, a relatively unstable decrease in rainfall is observed. The best statistical analysis result was observed with the LSTM model; the model’s accuracy was 99%, showing that it was quite good at presenting the obtained precipitation, temperature, and water data. Meanwhile, the value of the root mean squared error (RMSE) was about 13, 15, and 20, respectively. Therefore, the study’s results highlight that the LSTM model was the most suitable among the artificial neural networks for forecasting the weather, rainfall, and water resources. This study will help weather forecasting, agriculture, and meteorological departments be effective for water resource forecasting.

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Section: Discussionmentioning

confidence: 99%